Design of French Teaching Effect Analysis Model Based on Random Forest Algorithm and Neural Network

Abstract

In order to improve the quality of French in colleges and universities, a French quality data analysis model is proposed integrating the personal information of teachers, students, and teaching methods. The random forest algorithm is used to measure and describe the feature correlation between teachers and students, the feature correlation between objects and target variables, and the more accurate feature classification method is used to filter and save the optimal feature subset. Through the long and short memory in the self-attention mechanism, the neural network combines the portrait information of teachers and students as well as the French teaching information and carries out an in-depth analysis of the relevant data. Then, a more accurate conclusion is drawn on the correlation between the quality of French teaching and the relevant parameters. The empirical analysis results show that the proposed model effectively analyzes the relevant factors and rankings that affect the quality of French teaching at this stage and obtains more reasonable French teaching data to improve the quality of French teaching.