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Depicting the online learning process of student users from multiple angles can help implement deep learning and effectively improve their online learning quality, and it’s a practical and very meaningful work to mine the data burying in the topic discussion texts of online learning platforms so that useful information could be extracted and attained to help teachers better understand students’ learning sentiments and assist students to know of the learning status of their peers. However, in existing conventional sentiment analysis methods, the sample data with uncategorized tags are still labelled manually, and such work is usually time consuming and inefficient. In view of these defects, this paper aims to study the classification of college students’ learning sentiments based on the topic discussion texts of online learning platforms. In the beginning, this paper gave the overall structure of the proposed college student Learning Sentiment Classification (LSC) algorithm, and discussed the similarity between the topic discussion content and the teaching content. Then, this paper proposed to integrate Convolution Neural Network (CNN) with the Long-Short Term Memory (LSTM) network to build the said LSC model, so as to merge the advantages of the two and improve the accuracy of learning sentiment rating. After that, embedding layers of static words and non-static words were introduced into the proposed model for the purpose of realizing the mining of specific textual information while enhancing the semantic expression ability of the words. At last, experimental results verified the effectiveness of the proposed model.

Knowledge networks play an important role in the process of knowledge acquisition and sharing by students. An analysis of their complex structural features is required for the connectivity between students and knowledge. Existing research lacks insight into the internal structural features of knowledge networks constructed from expertise. There is also a lack of effective methods for constructing personalised knowledge networks for students' cognitive states. This paper analyses the categories and structures of expertise for students' cognitive states, and presents in detail a grey prediction algorithm to identify students' cognitive states. Then, the paper presents a typological description of the knowledge nodes in the expertise network for students' cognitive states, and analyses the knowledge network structure from the perspectives of paths and statistical properties. After that, the paper gives a method for analysing the knowledge flow of the expertise network. The experimental results validate the effectiveness of the proposed method.

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In the Innovation and Entrepreneurship (I&E) education of some higher vocational colleges, there’s a common problem: serious disconnection between professional education and practical education, which may result in poor quality of I&E education. With the help of I&E education quality evaluation, we can figure out the distribution and utilization of education resources, discover and solve problems during the teaching process in time, and optimize and adjust teaching content and methods in a targeted manner. Student behavior analysis can reveal the actual needs and questions encountered by students during I&E education, thereby attaining more pertinent and pragmatic evaluation results. For this reason, this paper aims to study the I&E education quality evaluation by means of modelling students’ behavior sequences. At first, a student I&E behavior sequence feature extraction module was created based on attention mechanism, and the student I&E ability level feature extraction layer and student I&E ability level feature evolution layer contained in the module structure were introduced in detail. Then, the data sources of I&E education quality evaluation based on students’ I&E behavior analysis were given, the I&E behavior sequences of students were modeled based on the single-sequence first-order linear differential equation model GM(1,1), and the established model was applied to I&E education quality evaluation. At last, experimental results verified the validity and accuracy of the proposed method.