If you only compare the text code string of BIG or ASIC, it is almost impossible to obtain the semantic similarity of the text, such as “Today is sunny.” and “It is sunny today.” Between the two strings, it is difficult to achieve a comparison of its semantics without the depth machine Learning. Moreover, it is even more impossible to compare its semantic similarity using traditional methods between the strings “Today is sunny.” and “the sun is warm in Winter.” If we use any neural network architecture to compare the above strings, it is hard to achieve sufficiently accurate comparison results of the text semantic similarity. Therefore, the comparison algorithm, combined with extraction of the text semantic feature string from the process of premachine learning and postmachine learning, has been widely used for the current text similarity comparison in recent years.
Liu Sihua et al. (2020) have conducted indepth research on the modal semantic comparison of “neng” and “hui” and the modal semantics of related words in Chinese language. If they are translated into English at the same time, the word “will” can be used to express the modality, but you can basically understand the comparison results of “active future time” and “passive future time” in Chinese semantics when using machine learning analysis[1]. Wang Youliang (2019) analyzes the problem of the strength and weakness of adjectives in Chinese language. For example, “warm”, “hot”, “fiery” and other adjective judgments that express subjective temperature should clearly distinguish the strength of their language[2]. Zhu Jing (2020) analyzes the comparison model of semantic types and expression methods under the condition of SinoRussian mutual translation. Research on semantic realization of crosslanguage machine translation technology is also conducted in other related documents have also conducted[3]. Yan Bing et al. (2020) study the semantic analysis of the SinoUS trade war international diplomatic discourse where there should be different expressions in the complex context of the field of machine semantic analysis[4]. With the comprehensive analysis of relevant literature, you will find that it is generally aimed at the realization of specific functions under a single function or a single application scenario in the current semantic analysis research based on machine learning and machine translation. It intends to explore a universal machine learning semantic analysis system that can provide high satisfaction with the current background research results in this paper.
In the early stage, it was impossible to use the semantic function library to support the big data of the related semantic comparison process as the data structure of the function library was difficult to get an effective and efficient setting. Chinese words like nouns, pronouns, verbs, adverbs, adjectives, prepositions, etc. are equipped with relatively independent and almost no semantic evaluation indicators of coincidence. Especially in complex grammatical environments where nouns are used as verbs, adjectives or prepositional prepositions, even the use of machines to judge their true partofspeech meanings is going to be a complex calculation. In some studies, the multilevel fuzzy comparison method is used to build the semantic function library where the partofspeech comparison module is used to classify the part of speech of the input word firstly, and then judge the semantic context based on its context and the secondlevel library.
It focuses on the direct fuzzy implementation of semantics where the output target of the semantic function library is not directed at the judgment output of the humanmachine interface but using a semantic depth code index for semantic recognition for subsequent machine learning modules. For example, Figure 1.
Thus, each fixed word in the semantic recognition library is converted into a 5digit semantic recognition code. This recognition code does not provide data support during the actual rigidity comparison process, but it is sufficient to provide the process of isomorphism support for natural text heterogeneous data in machine learning.
In the semantic recognition library, there may be a situation where a fixed word corresponds to multiple semantic recognition codes, such as the word “observation”, which may correspond to the weak semantic strength option of the noun, or may correspond to the strong semantic strength option of the verb. And it may also be used as a nounturning or verbtonoun application. This requires machine learning judgment based on contextual semantic filtering with the support of convolutional neural networks. This judgment mode will be analyzed in the following.
It's not demanded to have the semantic function characteristics of each word in the modern Chinese dictionary during the design process of the actual semantic function library. It only needs to include the semantic potential of 20003000 common words to provide comparative data support for the semantic potential of most Chinese language words, that is, there are about 800012000 comparative correlation functions in the semantic function library.
As shown in Figure 2, two character strings with unlimited length to be compared are entered in the system. In the strong convolution and stream input mode, a fuzzy neuron convolution network (see 2.1) is used to realize the process of generating its semantic string with the support of the semantic function library. The semantic string generated by the algorithm is subjected to a second analysis to obtain a time domain feature string by using the Fourier transform function as the core basis function of the frequency domain feature analysis module (see 2.2 for details). Then the two columns of feature strings go through a fuzzy neuron convolution network (see 2.3 for details) to obtain a comparison value Double result. Finally, a defuzzy module (see 2.4 for details) is used for defuzzy calculation, and a common formatted output module is used for output of the comparison results. The integrated algorithm uses a total of 2 fuzzy neuron convolutional networks to conduct a semantic comparison of the two columns of strings, which can minimize the computing power requirements of each neuron network to improve system efficiency.
It's operated a doubleloop convolution stream data comparison mode in the semantic function library where the comparison string is traversed separately to obtain the corresponding result and output for each record input of the semantic function library. This module belongs to a typical fuzzy neuron deep convolutional network algorithm with the core control variable of the pointer variable of the semantic function library, and the secondary control variable of the pointer variable of the twocolumn comparison string. The output variable is the semantic string data for the two column comparison string pointers. See Figure 3 for details.
In Figure 3 the functional design, two comparison strings are independently compared where a traversal pointer i for the comparison function library is implemented. For each i, a comparison string is used according to the target string length in the comparison function library. The pointer j performs characterbycharacter traversal on the comparison character string to form a comparison string pointer. That is, for the library input and the comparison string input, the character string length is equal. This article limits the length of each comparison string to no more than 4 characters or 8 bytes. For the only two inputs of the ancient fuzzy neural network, the length of which does not exceed 8 bytes of Bit data. However, the input data should be deeply convolved because the system needs to fully consider the context. And the doubleloop convolution method where ring A and ring B are four modules, each module is designed according to the hidden layer structure of 3, 7, 13, 5, and 1, and the nodes of each convolution module are carried out according to the method of highorder polynomial regression B. The function can be written as:
The module inputs an 8byte bit variable and outputs a 4byte Double variable. The hidden layer performs node design according to the hidden layer structure of 3, 7, and 3, and uses linear functions for the nodes. The function can be written as:
The output module integrates the output of the three convolution modules A1, B1, and B4 which are all Double variables. The statistical significance of this module fully binarizes the three sets of input data for the application of management. The hidden layer should reach a sufficient depth, so a 5layer hidden layer design is adopted, and the node design is performed according to the hidden layer structure of 5, 17, 31, 13, and 3. The function can be written as:
In the output module of the semantic string, when the result is close to 1.000, it will read the characteristic semantic identification code and the comparison string pointer generates the semantic identification code sequence of the position of the comparison string pointer according to the output of comparison result in real time, and when the result is close to 0.000, the semantic identification code corresponding to the comparison string pointer will be defined as 0. When the semantic identification code corresponding to the comparison string pointer already exists, the arithmetic average method will give the average result. Even if the machine learning semantic string generated by the algorithm cannot be checked from the semantic function library, it is sufficient for the subsequent three modules to generate machine learning results.
Table 1 can be obtained with the combination of the submodule design of this module.
Summary Table of Design Parameters of Semantic Function Library Comparison Module
Submodule  Input node  Output node  Hidden layer  Total number of nodes  Node function 

Library input  1×64bit  1×Double  3′ 7′ 3  15 

Compare string input  
Convolution A1  2×Double  1×Double  32  
Convolution A2  2×Double  1×Double  32  
Convolution A3  1×Double  1×Double  31  
Convolution A4  1×Double  1×Double  3′ 7′  31 

Convolution B1  2×Double  1×Double  13′ 5′  32  
Convolution B2  2×Double  1×Double  1  32  
Convolution B3  1×Double  1×Double  31  
Convolution B4  1×Double  1×Double  31  
5′ 17′  
Output  3×Double  1×Double  31 ′ 13′  73 

3 
The essence of the machine learning semantic string generated in 2.1 is a timedomain function which calibrates the semantic identification code information on the character sequence pointer sequence of the input string. But the string still has the domain specificity at a certain time. Therefore, the statistical significance of the frequency domain feature analysis module aims to weaken the specificity in the time domain so as to obtaining a frequency domain feature data. This module needs to perform a frequency domain feature extraction calculation process based on time domain data. And this process can be realized by one Fourier transform.
Firstly, the semantic identification code is obtained according to the pointer T of the semantic string, and the frequency domain features are extracted based on the Fourier transform:
After the feature function is obtained, it will be divided according to the total length of the pointer t, and the result will be extracted to form its frequency domain feature string.
This process is a rigid computing process and does not involve any machine learning algorithms. That is to say, this paper is to conduct a data management process based on rigid algorithm between two fuzzy neuron network modules.
Two columns of frequency domain feature strings, namely frequency domain feature string A and frequency domain feature string B, are input into the core comparison module, which is also a fuzzy neuron network convolution algorithm module. See Figure 4 for details.
The core algorithm of the fuzzy process of this module can judge the length of two frequency domain feature strings, and use the difference method to convert the frequency domain feature strings into equal length. Then, two input strings are formed according to the converted characteristic string pointer as the control variable for a 4module convolution module, in which the statistical significance of convolution A and convolution B integrate the input string data (Long type Variable) into the convolution cycle and the statistical significance of convolution C and convolution D provide a double data for each output module. Finally, an arithmetic average result is provided for all comparison results under the condition of equal length pointers. The result is the fuzzy comparison result of the two compared strings.
The idea of the submodule of this module is similar to the semantic function library comparison module, in which two input strings are managed by linear regression function with the hidden layer structure 3, 7, 3 and the node function like function (2). Four convolution modules perform node management according to the highorder polynomial regression function with the hidden layer structure 3, 7, 13, 5, and the node function like function (1). One output module performs grounding sheet management according to the binary regression function with the hidden layer structure 3, 7, 3 and the node function like function (3). Therefore, a summary table of the actual design parameters of the module is shown in Table 2.
Summary Table of The Actual Design Parameters Of The Module
Submodule  Input node  Output node  Hidden layer  Total number of nodes  Node function 

Input string A  1×Long  1×Double  3′ 7′ 3  15 

Input string B  
ConvolutionA  2×Double  1×Double  3′ 7′ 13 ′ 5′ 1  32 

Convolution B  2×Double  1×Double  32  
Convolution C  1×Double  1×Double  31  
Convolution D  1×Double  1×Double  31  
Output  2×Double  1×Double  3′ 7′ 3  16 

According to the previous analysis, the final output of the algorithm is the average result of the binarization after the deep algebraic average calculation, so the binarization feature of the final data is not significant. In other words, the final point of the model is basically concentrated on the [0,1] interval, and some results exceed this interval. The output result is a deeply defuzzy output result.
In the fuzzy process, two thresholds can be defined. When the output result is greater than a certain value M, the similarity of the two texts will be in a high confidence region, and when the output result is less than a certain value N, the similarity of the two ends of the text will be in the low Confidence zone, but it is still more likely to be in the [N, M] interval, at which point the system gives a weakly similar result. What it means is that the final formatted output of the algorithm in this paper contains three possible output judgment results. The semantics of the text at both ends are strongly similar, weakly similar, and dissimilar, and the output frequency of strong similar and dissimilar results should be ensured at least more than 80% for realization of the practical application scene adaptability of this algorithm.
Because the essence of the similarity evaluation results of text semantics is the user's subjective evaluation results, 100 volunteers, undergraduates or above students with a certain level of literary criticism in Chinese language and literature, international Chinese, and Chinese language education, are selected during the evaluation process. 50 pairs of text segments are selected for comparison to find the consistency of the system's evaluation results of the 50 pairs of text segments and the manual interpretation of volunteers. In order to judge the accuracy of the semantic similarity judgment of the system, the volunteers gave a subjective evaluation of very consistent (10 points), basically consistent (6 points), inconsistent (3 points) and completely inconsistent (0 points). In the final evaluation results, 100 volunteers gave very consistent evaluation 2,763 times (55.26%), basic consistent evaluation 1,326 times (26.52%), inconsistent evaluation 635 times (12.70%) and completely inconsistent evaluation 276 times (5.52%) in the corresponding 5,000 evaluation comparison process. The comprehensive judgment accuracy of this system (the proportion of very consistent and basically consistent synthesis) is 81.78%, and the comprehensive subjective score is 74.98 points (out of 100 points).
Among the 5000 evaluations, the system gave 1031 strong similarity evaluations, accounting for 10.62%, 391 weak similarity evaluations, accounting for 7.82%, and 3578 dissimilarity evaluations, accounting for 71.56%. Among them, the sum of strong similarity evaluations and dissimilarity evaluations was 4,609, accounting for 92.18%, which met the design requirements of this paper (see 2.4 for details).
The system focuses on the evaluation of the semantic similarity of text in the Chinese language, and achieves a comparison result of 81.78% of the accuracy of manual judgment. Only 5.52% of the volunteers believe that the system judgment result is completely inconsistent with the manual judgment result. The accuracy of achieving this judgment is still more advanced than the judgment accuracy of related literature for a single judgment target as the natural language semantic judgment based on machine learning is still a cuttingedge topic. The system, a general semantic judgment algorithm, can still be improved through further improvement of the semantic function library and deeper training of two sets of judgment neural network machine learning modules.
Summary Table of Design Parameters of Semantic Function Library Comparison Module
Submodule  Input node  Output node  Hidden layer  Total number of nodes  Node function 

Library input  1×64bit  1×Double  3′ 7′ 3  15 

Compare string input  
Convolution A1  2×Double  1×Double  32  
Convolution A2  2×Double  1×Double  32  
Convolution A3  1×Double  1×Double  31  
Convolution A4  1×Double  1×Double  3′ 7′  31 

Convolution B1  2×Double  1×Double  13′ 5′  32  
Convolution B2  2×Double  1×Double  1  32  
Convolution B3  1×Double  1×Double  31  
Convolution B4  1×Double  1×Double  31  
5′ 17′  
Output  3×Double  1×Double  31 ′ 13′  73 

3 
Summary Table of The Actual Design Parameters Of The Module
Submodule  Input node  Output node  Hidden layer  Total number of nodes  Node function 

Input string A  1×Long  1×Double  3′ 7′ 3  15 

Input string B  
ConvolutionA  2×Double  1×Double  3′ 7′ 13 ′ 5′ 1  32 

Convolution B  2×Double  1×Double  32  
Convolution C  1×Double  1×Double  31  
Convolution D  1×Double  1×Double  31  
Output  2×Double  1×Double  3′ 7′ 3  16 

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Mobile Teaching Assistant System Based on Ordinary Differential Equations Financial Crisis Early Warning Model of Listed Companies Based on Fisher Linear Discriminant Analysis High Simulation Reconstruction of Crowd Animation Based on Optical Flow Constraint Equation Construction of Intelligent Search Engine for Big Data Multimedia Resource Subjects Based on Partial Least Squares Structural Equation 3D Animation Simulation of Computer Fractal and Fractal Technology Combined with DiamondSquare Algorithm Analysis of the Teaching Quality of Physical Education Class by Using the Method of Gradient Difference The Summation of Series Based on the Laplace Transformation Method in Mathematics Teaching Optimal Solution of the Fractional Differential Equation to Solve the Bending Performance Test of Corroded Reinforced Concrete Beams under Prestressed Fatigue Load Animation VR scene mosaic modeling based on generalized Laplacian equation Radial Basis Function Neural Network in Vibration Control of Civil Engineering Structure Optimal Model Combination of Crossborder Ecommerce Platform Operation Based on Fractional Differential Equations The influence of accounting computer information processing technology on enterprise internal control under panel data simultaneous equation Research on Stability of Timedelay Force Feedback Teleoperation System Based on Scattering Matrix BIM Building HVAC Energy Saving Technology Based on Fractional Differential Equation Construction of comprehensive evaluation index system of watersaving irrigation project integrating penman Montei the quation Human Resource Management Model of Large Companies Based on Mathematical Statistics Equations Data Forecasting of AirConditioning Load in Large Shopping Malls Based on Multiple Nonlinear Regression Analysis of technical statistical indexes of college tennis players under the winlose regression function equation Automatic extraction and discrimination of vocal main melody based on quadratic wave equation Analysis of wireless English multimedia communication based on spatial state model equation Optimization of Linear Algebra Core Function Framework on Multicore Processors Optimisation of Modelling of Finite Element Differential Equations with Modern Art Design Theory Mathematical function data model analysis and synthesis system based on shortterm human movement Human gait modelling and tracking based on motion functionalisation The Control Relationship Between the Enterprise's Electrical Equipment and Mechanical Equipment Based on Graph Theory Financial Accounting Measurement Model Based on Numerical Analysis of Rigid Normal Differential Equation and Rigid Functional Equation Mathematical Modeling and Forecasting of Economic Variables Based on Linear Regression Statistics Nonlinear Differential Equations in Crossborder Ecommerce Controlling Return Rate Differential equation model of financial market stability based on Internet big data 3D Mathematical Modeling Technology in Visualized Aerobics Dance Rehearsal System Children’s cognitive function and mental health based on finite element nonlinear mathematical model Fractional Differential Equations in Electronic Information Models BIM Engineering Management Oriented to Curve Equation Model Leakage control of urban water supply network and mathematical analysis and location of leakage points based on machine learning Analysis of higher education management strategy based on entropy and dissipative structure theory Prediction of corporate financial distress based on digital signal processing and multiple regression analysis Mathematical Method to Construct the Linear Programming of Football Training Multimedia sensor image detection based on constrained underdetermined equation The Size of Children's Strollers of Different Ages Based on Ergonomic Mathematics Design Application of Numerical Computation of Partial Differential Equations in Interactive Design of Virtual Reality Media Stiffness Calculation of Gear Hydraulic System Based on the Modeling of Nonlinear Dynamics Differential Equations in the Progressive Method Knowledge Analysis of Charged Particle Motion in Uniform Electromagnetic Field Based on Maxwell Equation Relationship Between Enterprise Talent Management and Performance Based on the Structural Equation Model Method Term structure of economic management rate based on parameter analysis of estimation model of ordinary differential equation Influence analysis of piano music immersion virtual reality cooperation based on mapping equation Chinese painting and calligraphy image recognition technology based on pseudo linear directional diffusion equation Label big data compression in Internet of things based on piecewise linear regression Animation character recognition and character intelligence analysis based on semantic ontology and Poisson equation Design of language assisted learning model and online learning system under the background of artificial intelligence Application of machine learning in stock selection Comparative analysis of CR of ideological and political education in different regions based on improved fuzzy clustering Action of Aut( G ) on the set of maximal subgroups ofp groupsThe internal mechanism of corporate social responsibility fulfillment affecting debt risk in China: analysis of intermediary transmission effect based on degree of debt concentration and product market competitive advantage Study on transmission characteristics in three kinds of deformed finlines based on edgebased finite element method Asymptotic stability problem of predator–prey system with linear diffusion Research on loyalty prediction of ecommerce customer based on data mining Algebraic Equations in Educational Model of College Physical Education Course Education Professional English Translation Corpus Under the Binomial Theorem Coefficient Geometric Tolerance Control Method for Precision Machinery Based on Image Modeling and Novel Saturation Function Retrieval and Characteristic Analysis of Multimedia Tester Based on Bragg Equation Semiparametric Spatial Econometric Analysis of Household Consumption Based on Ordinary Linear Regression Model Video adaptive watermark embedding and detection algorithm based on phase function equation English Learning Motivation of College Students Based on probability Distribution Scientific Model of Vocational Education Teaching Method in Differential Nonlinearity Research on mobile Awareness service and data privacy Protection based on Linear Equations computing protocol Vocal Music Teaching Model Based on Finite Element Differential Mathematical Equations Research on threat assessment problems of island air defence system based on the leaderfollower model Studying a matching method combining distance proximity and buffer constraints The trend and influence of media information Propagation based on nonlinear Differential equation Research on the construction of early warning model of customer churn on ecommerce platform Study on inefficient land use determination method for cities and towns from a city examination perspective A sentiment analysis method based on bidirectional long shortterm memory networks Evaluation of ecosystem health in Futian mangrove wetland based on the PSRAHP model A study of local smoothnessinformed convolutional neural network models for image inpainting Towards more efficient control of the ironmaking blast furnace: modelling gaseous reduction of iron ores in H_{2}N_{2} atmosphere Algorithm of overfitting avoidance in CNN based on maximum pooled and weight decay Mathematical Calculus Modeling in Improving the Teaching Performance of Shot Put Application of Nonlinear Differential Equation in Electric Automation Control System Higher Mathematics Teaching Curriculum Model Based on Lagrangian Mathematical Model Decisions of competing supply chain with altruistic retailer under risk aversion Optimization of Color Matching Technology in Cultural Industry by Fractional Differential Equations The Marketing of Crossborder Ecommerce Enterprises in Foreign Trade Based on the Statistics of Mathematical Probability Theory Application of Linear Partial Differential Equation Theory in Guiding Football Scientific Training The Evolution Model of Regional Tourism Economic Development Difference Based on Spatial Variation Function System Model of Shipping Enterprise Safety Culture Based on Dynamic Calculation Matrix Model The Inner Relationship between Students' Psychological Factors and Physical Exercise Based on Structural Equation Model (SEM) Analysis and Research on Influencing Factors of Ideological and Political Education Teaching Effectiveness Based on Linear Equation Fractional Differential Equations in Sports Training in Universities Examination and Countermeasures of Network Education in Colleges and Universities Based on Ordinary Differential Equation Model Higher Education Agglomeration Promoting Innovation and Entrepreneurship Based on Spatial Dubin Model ChineseEnglish Contrastive Translation System Based on Lagrangian Search Mathematical Algorithm Model