Software Engineering Teaching Quality Evaluation Model Based on Big Data Fuzzy K-Means Clustering

Aiming at the problems of low accuracy, poor reliability and low efficiency of current quality assessment models, Propose a teaching quality evaluation model based on the fireworks algorithm to optimize k-means clustering; Optimize the k-means clustering algorithm using the fireworks algorithm with balanced global and local search capabilities, Use the obtained data results as the initial clustering center point of the k-means clustering algorithm, Solving the problem that k-means clustering algorithm is prone to falling into local optimality. he evaluation performance of the proposed model was verified from two aspects: accuracy and modelling time. The results show, the evaluation accuracy of the proposed model for different courses is greater than 92%, The evaluation results are highly reliable, the average modelling time is less than 32.1ms, and the efficiency is high.