At present, traditional algorithms in IT recruitment information classification have long-distance dependence, and cannot highlight the impact of IT job keywords on text classification features. In this study, a multi-layer text classification model combining two-way long-term and short-term memory network BiLSTM and attention mechanism is applied to the classification of recruitment information. The model includes the one-hot word vector input layer, BiLSTM layer, attention mechanism layer, and output layer. One-hot layer builds a recruitment dictionary, which saves a lot of training word vector time; the BiLSTM layer can obtain more semantic information of different distances in the context; and the attention mechanism layer transforms the weights of the data encoded by BiLSTM enhancing the serialization learning task. The results show that the classification accuracy of IT recruitment information based on this model reaches 93.36%, which is about 2% higher than other models. The model analyzes the requirements of different positions on the ability of the employed in a more targeted manner, and realizes the classification of recruitment information in different positions, which is of great significance to the employment guidance of college graduates.