Fast Retrieval Method of Learning Resources for Python Online Courses

To improve the efficiency of resource retrieval, this study proposes a fast retrieval method for Python online course learning resources. First, according to the time of collecting learning resources, calculate the average retrieval probability of learning resources, and use the filter to preprocess the learning resources of Python online courses. Then, calculate the correlation between the retrieval words, calculate the weight value of the learning resources according to the semantic similarity, and extract the key features of the learning resources through the query of the retrieval words and the Lyapunov theorem. Finally, according to the value range of satisfaction component, the learning resources retrieval space is established, and then the rapid retrieval of resources is realized. The experimental results show that this method can achieve frequency retrieval, sequence retrieval, and autocorrelation retrieval, and control the retrieval time of learning resources within 0.2 s, indicating that this method has high retrieval timeliness and can reduce unnecessary computation and resource loading time.