Evaluation Model of English Teaching Effect Based on Neural Network Algorithm and Support Vector Machine

The optimize the analysis of support vector machines in response to the problem that traditional English teaching models cannot solve the accuracy and efficiency of student English teaching effectiveness evaluation, nor can they meet the requirements of English teaching effectiveness evaluation. Firstly, a multi-indicator English teaching effectiveness evaluation system with teachers and students as the main body was constructed using relevant theories, and the indicators were divided according to the data requirements of English teaching effectiveness evaluation indicators to reduce support vector machines in interference factors. Then, the neural network algorithm is used to solve the optimal solution of the kernel function parameters and regularization parameters of the support vector machine, forming a support vector machine scheme, and the support vector machine results are comprehensively analyzed. MATLAB simulation shows that neural network algorithms and support vector machines have the best evaluation accuracy and short evaluation time for English teaching effectiveness under certain evaluation criteria.