<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In order to better improve the level and quality of rural family education, this paper adopts text mining technology to interpret the relevant data of rural family education under the background of deep learning. On the basis of deep learning algorithms, web crawler technology is used to obtain rural family education research data. It is found that there are null values and repetitive values in the acquired research data, so it is necessary to further clean the research data. After washing, the combined Bi-LSTM-CRF algorithm is used to process the data for Chinese word segmentation, and at the same time, the stop word list is improved in order to achieve the best word segmentation effect. The LDA thematic model was used to determine the influencing factors of rural family education, and then the linear regression algorithm was used to analyze rural family education empirically. Rural teachers with different education levels were not significantly different in Q1 (1.608), Q2 (0.117), Q3 (2.019), Q4 (1.733), and Q5 (2.042), according to the results. The adjusted R<sup>2</sup> is 0.133, indicating a fair fit between the model obtained from the regression analysis and the actual data, and the regression coefficient of family education is 0.411 after eliminating the confounding interference of grade and political appearance. This study purposefully proposes a path for the innovative development of rural family education, which can provide a theoretical reference program for the development and construction of modern rural family education.</p>
</abstract>

In order to better improve the level and quality of rural family education, this paper adopts text mining technology to interpret the relevant data of rural family education under the background of deep learning. On the basis of deep learning algorithms, web crawler technology is used to obtain rural family education research data. It is found that there are null values and repetitive values in the acquired research data, so it is necessary to further clean the research data. After washing, the combined Bi-LSTM-CRF algorithm is used to process the data for Chinese word segmentation, and at the same time, the stop word list is improved in order to achieve the best word segmentation effect. The LDA thematic model was used to determine the influencing factors of rural family education, and then the linear regression algorithm was used to analyze rural family education empirically. Rural teachers with different education levels were not significantly different in Q1 (1.608), Q2 (0.117), Q3 (2.019), Q4 (1.733), and Q5 (2.042), according to the results. The adjusted R2 is 0.133, indicating a fair fit between the model obtained from the regression analysis and the actual data, and the regression coefficient of family education is 0.411 after eliminating the confounding interference of grade and political appearance. This study purposefully proposes a path for the innovative development of rural family education, which can provide a theoretical reference program for the development and construction of modern rural family education.

A Systematic Study on the Dilemma and Innovative Path of Rural Family Education Development in the Context of Deep Learning

Hainan Vocational University of Science and Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In order to better improve the level and quality of rural family education, this paper adopts text mining technology to interpret the relevant data of rural...

A Systematic Study on the Dilemma and Innovative Path of Rural Family Education Development in the Context of Deep Learning

Data publikacji: 15 maj 2024

Zakres stron: -

Otrzymano: 04 lut 2024

Przyjęty: 12 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-1119

Słowa kluczowe
Bi-LSTM-CRF, LDA subject model, Regression algorithm, Web crawler, Rural family education

© 2024 Jun Li, et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A Systematic Study on the Dilemma and Innovative Path of Rural Family Education Development in the Context of Deep Learning

Data publikacji: 15 maj 2024

Zakres stron: -

Otrzymano: 04 lut 2024

Przyjęty: 12 kwi 2024

DOI: https://doi.org/10.2478/amns-2024-1119

Słowa kluczoweBi-LSTM-CRF, LDA subject model, Regression algorithm, Web crawler, Rural family education

© 2024 Jun Li, et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
Bi-LSTM-CRF, LDA subject model, Regression algorithm, Web crawler, Rural family education