With the development of modern information technology, the ideas and behavior of university managers have changed a lot. Under the new situation, university education management needs to make some changes to better meet the needs of university management. First, the information construction of university education management can better realize accurate management. The traditional education management of colleges and universities mostly adopts the way of unified management, without considering the differences of students' personality, ability and quality, growth environment and so on. Therefore, it is easy to have problems such as low management efficiency and inadequate management in the process of management. In order to promote the information construction of education management, college education managers can use big data and other technologies to investigate and summarize the use of students' On-campus learning platform, the official microblog message information of the school, etc. The visual processing of these data can help managers further grasp the behavior of students, so as to implement differentiated management for different types of students, and make the education management of colleges and universities more targeted and accurate [1]. Second, the information construction of educational management in Colleges and universities can make the educational management work develop in a scientific direction. Under the traditional education management mode, managers generally make management decisions according to the general situation of the managers. In thermodynamics, entropy is defined as the ratio between the heat Q absorbed by the object and the temperature T of the object. In microscopic theory, entropy is usually understood as the increase of chaos. If the number of states of a system is limited more, the order degree of the system will be higher and the entropy will be smaller. On the contrary, the less the number of states of the system is limited, the disorder degree of the system will be higher and the entropy will be larger. As well as previous management experience, it is easy to experience. In this process, the previous experience of managers may not be applicable to the current situation, which affects the development of management work [2]. Under the background of educational management informatization, educational managers can use the information in the database to make more reasonable and scientific management decisions and formulate educational management plans that are more in line with the development needs of students and teachers, which can effectively improve the level of school education management [3].

For example, ice below zero is low entropy, and after ice melts into water, water has fluidity, water molecules will collide with each other, but it has high entropy. Austrian physicist Boltzmann was the first to associate entropy with the description of the state probability of molecular irregular motion, and expounded the statistical properties of the second law of thermodynamics. He proposed the famous entropy law

Extremum: If the object has N states, the probability of occurrence of a state q is

Additivity: If system a and system B are completely independent, the entropy of composite system AB is equal to that of subsystem a, plus that of subsystem B:

If an impossible event _{n}_{n}

Enforceability: If system a and B are statistically related, and

Symmetry: The entropy of the system is independent of the order in which its state probability p appears.

Buglakov A I and others proposed the entropy method to evaluate the complexity of manufacturing enterprises. This method can evaluate the complexity of simple enterprise systems according to objective data, but it is not suitable for evaluating the complexity of enterprise production process, and the evaluation cost is too high [6]. Entropy theory is also used in system engineering and process management. He Q applies entropy to the research of enterprise organization ERP. They regard entropy as the degree of confusion in the management of the organization. With the development of enterprise organization ERP to the mature stage, the degree of confusion of the enterprise becomes lower and lower [7]. Hao C and others took information entropy as an index to measure the complexity of supply chain operation, and pointed out that this method has strong flexibility and can reflect the changes of supply chain [8].

Based on the current research, this paper proposes a method based on entropy and dissipative structure theory. Firstly, we determine the connection length _{ab}_{1},
_{1} (_{a}_{a}

In 1865, the German physicist Clausius called the ratio of heat absorbed by matter to temperature in the reversible process entropy, which Clausius defined as follows:
_{0} is the initial state, P is the end state, _{0}, _{0}, _{0} is the change of entropy. If the system goes through a rollback process. The entropy change mentioned above, ie the integral value, is a physical quantity that does not depend on the integral path, but only on the initial and final states of the system, and is a function of the state of the system. The second law of thermodynamics is now expressed in the form of entropy: During the reverse process, the entropy change of the system is equal to the ratio of the heat absorbed in the system to the temperature of the heat source. In an irreversible process, the entropy change is greater than the ratio of heat to temperature. It is concluded that the entropy change is greater than zero during the irreversible thermal process in the system, ie there is a principle of entropy increase. When the return process takes place in the system, the entropy is zero, the irreversible processes are all processes from the unbalanced state to the equilibrium state, and the equilibrium state corresponds to the maximum entropy [9].

We introduce the concept of entropy to management science and gain the concept of management entropy. Any kind of management organization, called management entropy, refers to a systemic policy approach, etc., and always shows an irreversible process in which the efficiency of a relatively closed organization gradually decreases and the inefficient energy increases continuously. This is a law that reduces the efficiency of organizational management.

The irreversibility of management system is similar to that of various systems in nature, We can define “management entropy” with the help of “entropy” in thermodynamics. From the perspective of management efficiency, I believe that entropy is a measure of the management efficiency presented by the organization, system, policy and method of the system in the process of relatively closed system movement. The increase of system entropy means the continuous reduction and consumption of “management efficiency” [10].

The theory of dissipative structure was proposed by the Belgian physicist Prigogine in 1969. The theory, called dissipative structure theory, refers to a nonlinear open system that is far from equilibrium. time, space, and function. Prigogine called it a “spreading structure” because this new orderly structure was far from equilibrium. The theory of dissipative structure is a science that studies its properties, formation, stability, and laws of evolution [11].

The theory of dissipative structure divides macroscopic systems into isolated systems, closed systems, and open systems. An open system has three states: equilibrium, near equilibrium, and far from equilibrium.

The continuing education system of colleges and universities is an open system. The continuing education system of colleges and universities does not exist in isolation, but in a dynamic and open state of continuous interaction with the outside world and continuous input and output. First, in the integration and coordination of continuing education resources, there is an interactive relationship between schools and society and between schools and specific implementation departments; Second, it shows the openness of the object of continuing education, that is, the socialization of the source of the object of education. Therefore, the management system of continuing education in Colleges and universities is a complex open system closely related to the outside world.

The continuing education system in Colleges and universities is far from equilibrium. Since the reform and opening up, with the establishment of socialist market economy and the booming demand for all kinds of continuing education, continuing education is gradually developing towards a socialized, diversified and open pattern, and various non-governmental training institutions have actively involved in this field. By virtue of their new system and flexible mechanism, they give full play to their own advantages, bring vitality and vitality to the field of continuing education, and attract the attention of the society. Needless to say, this will drive the continuing education system of colleges and universities away from balance, and this imbalance has a growing trend [12].

The interaction between the elements in the continuing education system of colleges and universities is nonlinear. There are a large number of nonlinear interaction mechanisms among the elements of the continuing education system of colleges and universities. People's subjective initiative can not be measured by simple numbers. The social system is nonlinear in essence. In all the work of continuing education management in Colleges and universities, there are strong psychological, emotional, intellectual and other spiritual factors. Whether in terms of quality or quantity, the constituent elements can not be a simple linear relationship.

We have used this method for reference and made minor modifications to the calculation process of tissue entropy. The specific calculation method is as follows:

First, determine the connection length _{ab}

Secondly, the total number of micro states of the system _{1} is calculated,

Finally, calculate the organizational structure entropy between any two elements at the same level vertically and horizontally: _{1} (_{a}_{a}

The entropy method is used to measure and determine the expected content of information, which has subsequently become a very important method in social science and physics. In information theory, entropy is represented by uncorrelated _{a}

Among them, S represents the size of the information entropy, h represents the possible h states of the system, and the probability of each state appearing is pl. In the comprehensive evaluation, the information entropy evaluation can be applied to obtain the order degree of the system information and the utility value of the information. The larger S is, the higher the disorder degree of information is, and the smaller the utility value of information is; on the contrary, the smaller S is, the lower the disorder degree of information is, and the greater the utility value of information is [13]. When

The evaluation grade of weight can also be obtained by entropy method. The N attributes of M indicators form matrix R.

The result of matrix _{m}_{m}_{n}

The entropy weight _{b}

As a method to evaluate the relative importance of attributes, entropy weight method can sensitively reflect the content of information. Therefore, in this study, the calculation method of entropy weight method is used for reference:

Firstly, if the operation of the system has m evaluation indexes and n evaluation objects, the following evaluation index eigenvalue matrix X can be established:
_{ab}

Secondly, X is standardized to obtain matrix

For profit and loss indicators:

Standardized processing can classify each attribute value into a dimensionless quantity, which is convenient for addition, subtraction, multiplication and division between attributes of different units for comparison.

Finally,

Where:

The calculation result of internal entropy is the sum of organizational entropy and team construction and management entropy. Where _{1} is the organization entropy and _{2} is the team building and management entropy [14]. The calculation results of tissue entropy are as follows:

The five randomly selected management teams are teaching building A and B, two teams, teacher management team and student management team. The specific structure analysis is shown in Figure 1.

Using the calculation method of organization entropy, first determine the connection length _{ij}

Calculation results of contact degree

The contact length is 1 | The contact length is 2 | The contact length is 3 | The contact length is 4 |
---|---|---|---|

(1,2) (1,3) (2,5) | (2,3) (2,4) (2,5) (3,6) | (1,5) (1,6) (1,7) | (6,7) (6,8) (7,9) (7,8) |

(2,5) (3,6) (3,7) | (2,6) (2,7) (2,8) (3,5) | (2,9) (4,8) (4,9) | |

(4,7) (4,8) | (3,5) (4,5) (6,7) (7,9) | (4,5) (4,6) (4,7) | |

(4,9) (5,6) (5,7) |

Then the structural entropy of the system:

Team building and management entropy include three indicators: the score rate of Q12 questionnaire, the proportion of undergraduates and the proportion of people taking the postgraduate entrance examination. The index value is shown in Figure 2.

First, substitute the index value into the matrix X mentioned in the previous chapter.

The result is:

Normalize the matrix to obtain the standard matrix

To sum up, internal entropy

The calculation indicators of external entropy flow include the proportion of team members participating in the meeting of the whole hospital, the number of communication between the team and teachers in a week, the number of performance appraisal of the team in a year, the proportion of team members commended by the whole school, the timeliness and accuracy of team members' feedback to teachers on students' achievements, and students' satisfaction [15].

The calculation method of external entropy is the same as that of team internal construction and management. The calculation results are as follows:

According to the calculation results of the entropy model, the internal entropy of the teacher's management team is 6.547 and the external entropy is 7.412. The external entropy is less than the internal entropy, but the gap is small. According to the dissipative structure model of team development mentioned above, the development of the group is in an orderly stage, which indicates that the team is achieving satisfactory performance. The small difference between internal entropy and external entropy also indicates that the team is in a mature stage and in a stable management mode. Student satisfaction is considered to be an important dimension of management team performance evaluation. Through the survey of student satisfaction, it is found that the student satisfaction rate of two teams is 100%, the student satisfaction rate of two teams is 99%, and the satisfaction rate of one team is 98.5%, which shows that the management team has good performance. This is just consistent with our research results, which shows that the management team in the case is in an orderly development stage with excellent performance. The entropy model of team performance evaluation has been verified in the case study. It has certain practical value and can be used as a work to evaluate team performance evaluation. In the case study, we found that the school management team can maintain the high performance of the team under the working conditions of great pressure and challenges, and the students have a high evaluation of its performance. What is the reason? As the previous theoretical summary of the management team, when the team is used in management, a stable management mode is formed, including personnel composition, three shift system, training and reward system. Although the management mode will be slightly transferred in different schools and different periods, some basic management methods have not changed. It may be this mature and stable management model that has an impact on the performance of the team and maximizes the performance of the team.

The management mode of school management team brings us profound inspiration. The stable management mode formed by the organization can not be easily changed. The change of the organization has its own laws to follow. The kind of reform that mechanically applies other organizational development models and runs counter to organizational development can only lead to poor efficiency, adaptability and other important characteristics of the organization, and finally eliminated. The complexity science represented by entropy and dissipative structure theory emphasizes the self-organization phenomenon in the development of organizations. Even if some organizations are organized at the beginning of the operation process, they also follow the law of self-organization in the subsequent operation process.

On the basis of entropy and dissipative structure, the existence of team entropy and dissipative structure is verified. Under the effect of entropy increase, the internal efficiency of the team decreases and eventually moves towards a state of disorder. If you want the team to be always full of vitality and maintain an orderly state, the team must expand its openness and actively introduce negative entropy flow. On the basis of team entropy, this paper analyzes the factors affecting team performance, judges the internal entropy flow and external entropy flow that cause the existence and evolution of team, and establishes the entropy model for evaluating team performance and the calculation method of entropy model. Through empirical research, the feasibility of team performance evaluation entropy model is proved, which provides a dynamic method for team performance evaluation. The study of the case team found that the development of the team has its own development law. A stable management model will have an impact on the performance of the team, while the blind reform of the management model will only make the team decline.

#### Calculation results of contact degree

The contact length is 1 | The contact length is 2 | The contact length is 3 | The contact length is 4 |
---|---|---|---|

(1,2) (1,3) (2,5) | (2,3) (2,4) (2,5) (3,6) | (1,5) (1,6) (1,7) | (6,7) (6,8) (7,9) (7,8) |

(2,5) (3,6) (3,7) | (2,6) (2,7) (2,8) (3,5) | (2,9) (4,8) (4,9) | |

(4,7) (4,8) | (3,5) (4,5) (6,7) (7,9) | (4,5) (4,6) (4,7) | |

(4,9) (5,6) (5,7) |

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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 edge-based finite element method Asymptotic stability problem of predator–prey system with linear diffusion Research on loyalty prediction of e-commerce 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 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 e-commerce platform Evaluation and prediction of regional human capital based on optimised BP neural network Study on inefficient land use determination method for cities and towns from a city examination perspective A sentiment analysis method based on bidirectional long short-term memory networks A study of local smoothness-informed convolutional 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Translation System Based on Lagrangian Search Mathematical Algorithm Model Genetic algorithm-based congestion control optimisation for mobile data network