为了解决卷积循环神经网络（convolutional recurrent neural networks, CRNN）手写汉字文本识别网络模型的训练参数大、文本识别率低等问题，提出一种基于注意力双向长短期记忆网络（based on attention bi-directional long short-term memory network, AT-BLSTM）和知识蒸馏（knowledge distillation, KD）技术的手写汉字识别方法。通过对AT-BLSTM网络的输入向量特征赋予不同的权重，使模型训练数据集更加高效、准确；通过KD技术将一个高性能的大模型获取的知识传输到一个小模型中，在确保模型准确性的同时，减少训练参数和内存占比，得到一个性能更优的轻量级训练模型。该方法通过多组实验对比，汉字识别准确率提高了6.7%，训练参数减少15.94 M。该网络模型识别准确率达到97.9%，汉字识别效果更好。

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.