With the rapid development of artificial intelligence-related technologies, especially the use of big data, an intelligent world is coming. In the era of intelligence, the traditional trading teaching work model is no longer adaptable. If it wants to survive the new wave of technological development, it must carry out a self-revolution in science and technology. This article aims to study the improvement and optimization of the current college education curriculum system by artificial intelligence equipment under the use of big data technology. To this end, this paper proposes a clustering algorithm for data analysis. Through the improvement of the clustering algorithm and using it in the reform of the education system of colleges and universities, the relevant education data is calculated with high performance and fed back to the teacher to improve the teaching method. At the same time, experiments are designed to analyze the performance of the algorithm and the feasibility of the teaching mode. The experimental analysis results in this paper show that the improved data analysis clustering algorithm has improved the data analysis ability in the teaching process by 37%, and the use of big data has increased the teaching quality score of colleges and universities by nearly 1 point. It can well promote the popularization of education informatization in the country and the improvement of teaching quality.

A novel Fuzzy Neural Network (FNN) teaching quality assessment model of physical education (PE) is presented at colleges and universities to enhance the validity of PE teaching quality evaluation. It is being done to enhance the accuracy of quality evaluations of PE instruction. In the first phase, out of 4 aspects of teaching material, teaching method, teaching attitude, and teaching effect, a multi-index assessment process of university physical education teacher performance based on the analytic hierarchy process (AHP) is created. The effectiveness of college PE instructors is assessed using this approach. The FNN model is used to develop a teaching quality assessment model for college PE courses. The FNN’s parameter is the score data, and the FNN’s output vector is equipped with better college PE (excellent, good, average, and low). In terms of assessing the instructional excellence of PE courses in colleges and universities, FNN has been proven to have superior classification accuracy, specificity, sensitivity, and F1 score when compared to other methods. When compared to other countries, this is the case. The proposed approaches resulted in a score of 96% for accuracy, 95% for specificity, 90% for sensitivity, and an F1 score of 94% for performance. The effectiveness of the proposed approach is shown by comparing the outcomes to those of standard physical education teaching strategies.

Data warehouse technology has been created because of China’s technological advancement and the increasing requirements of the educational sector. Physical assessments are treated as tests by many students. Institutions spend plenty of time every year through physical tests, yet the results are rarely shared with students. Teachers are impeded by the size and complexity of physical test data, finding it challenging to support experiments or judge individual students’ development. Students have trouble following up and delivering test-based feedback after instruction. In recent years, various researchers have offered insightful advice on how to build multidimensional database structures for such trouble. However, quality requirements alone are not adequate to guarantee quality in reality. So, this paper presents a novel Hypertuned wide polynet convolutional neural network (HWPCNN) framework in the data warehouse technology to attain the greatest performance in physical education quality management. In this paper, we first apply HWPCNNs for physical education quality management to analyze the accuracy and recall of the model. It is no secret that HWPCNN is now one of the most widely used deep learning techniques. When it comes to managing the quality of physical education, the HWPCNN’s local perception feature in the data warehouse technology allows it to achieve the best possible results. To validate the model’s performance, it is compared to other models and then improved further to increase its accuracy. The physical education resources are gathered as a raw dataset for this inquiry. The raw dataset is cleaned using the Z-score approach to get it ready for further data processing. Then, a sparse matrix approach is employed to build a data cube, while the proposed method is used to index multidimensional databases. To demonstrate that our work is of the best quality in managing physical education, performance metrics of the suggested method are also evaluated and compared with other traditional methods.