Teaching Quality Evaluation of Application-oriented Undergraduate College English Based on PSO-BP Neural Network Model

Teaching quality evaluation is one of the key points in teaching research. As the existing mathematical evaluation models are no longer as suitable as neural network for solving nonlinear problems in teaching quality evaluation. This paper establishes a teaching quality evaluation based on PSO-BP neural network model by integrating PSO(Particle Swarm Optimization) algorithm and BP(Back Propagation) neural network. To verify the effectiveness of the model, experiment was conducted by using evaluation data of a certain application-oriented undergraduate college English course. The data of 7 indexes in the evaluation system are used as the input, and the evaluation results are used as the output for model. The model of BP neural network is enhanced through PSO algorithm to train its parameters, weights and thresholds. Experimental results verify that the PSO-BP neural network model has better perform in terms of global optimization ability and convergence speed. The accuracy of PSO-BP model has reached about 98%, indicating that the use of this model can effectively evaluate teaching quality and has certain application and reference value in teaching quality evaluation.