In order to solve the problems of large training parameters and low text recognition rate of convolutional recurrent neural networks (CRNN) handwritten Chinese character recognition network model, a novel method for handwritten Chinese character recognition based on attention bi-directional long short-term memory network(AT-BLSTM) and knowledge distillation (KD) technology was proposed. By assigning different weights to the input vector features of AT-BLSTM network, the model training data set was more efficient and accurate. Through KD technology, the knowledge acquired from a large high-performance model was transferred to a small model, which ensured the accuracy of the model, reduced the training parameters and internal storage ratio, and obtained a lightweight training model with better performance. Through the comparison of multiple groups of experiments, the accuracy of Chinese character recognition is increased by 6.7%, and the training parameters are reduced by 15.94 M. The recognition accuracy of this network model reaches 97.9%, and the recognition effect of Chinese characters is better.