This work aims to adapt to the coming of a knowledge economy society and promote the improvement of China’s higher education system. It is necessary to establish a new management mechanism of college teachers’ performance evaluation to strengthen the quality of college teachers and improve the level of education and scientific research. Performance appraisal can be used to monitor the teaching staff scientifically and effectively to continuously improve and develop the college teacher system in China. This work first investigates the characteristics of performance evaluation of worldwide colleges, analyzes the development status of performance evaluation, and constructs a new performance evaluation index system through data and interviews. Then, based on the radial basis function neural network in artificial intelligence technology, a fine evaluation model of Chinese college teachers’ performance is established. Network training is adopted to analyze the previous performance evaluation to ensure that the final weight is obtained to minimize the sum of previous evaluation errors. Then, the index data of 61 teachers’ educational performance evaluation of X college from 2016 to 2021 are used for analysis and verification. The experimental results show that only 9.9% of the teachers in X college have excellent performance evaluation results, 29.5% of the teachers have medium evaluation results, and the statistical excellent rate is only 26.3%. Finally, the corresponding improvement suggestions and countermeasures are given for the low excellent rate of colleges.
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