Construction of English Resource Database Network Information Recommendation Model Based on LSTM Algorithm

With the popularization of the Internet and the rapid development of mobile devices and wireless networks, e-commerce has received widespread attention as an indispensable part of the information age. However, there are currently many defects and shortcomings in resource sharing mechanisms, as well as problems such as the lack of data mining technology, which have brought great difficulties to English teaching based on LSTM algorithms. Therefore, this research aims to build a complete, efficient and highly accurate English resource database network information retrieval platform based on the LSTM algorithm, and to cooperate with related systems. This article then tests the performance of the model. The test results show that the real-time performance of the LSTM-based recommendation algorithm is nearly 20% higher than that of the traditional algorithm; the personalization performance is 0.96, which is nearly 3 times higher than the 0.31 of the traditional algorithm. However, the complexity of the traditional algorithm is 0.5, while the LSTM-based recommendation algorithm is 0.86.