Enterprise Information Employee Attribute Association Mining Algorithm Based on Recurrent Neural Network

In order to help enterprises more accurately predict and respond to employee related issues, optimize human resource allocation, and achieve sustainable development, a recurrent neural network-based algorithm for mining employee attribute associations in enterprise information is proposed. Collect various types of information data within the enterprise, implement vectorization processing through TF-IDF method, and use the processing results as inputs for the RNN model. Build an RNN model to process input data in sequence, capture temporal dependencies and contextual information in the data, use the output of the RNN model to represent the characteristics of each employee, and mine the associations between employee attributes by comparing the similarities between these features. Introducing LSTM to solve the long-term dependency problem of RNN and optimize mining results. The experimental results show that the proposed algorithm has a high coverage of mining results and low memory usage, indicating its good application effect.