针对手写汉字中相似汉字的识别问题，构建了一种卷积神经网络( CNN) 模型，并给出了其网络拓扑结构，通过随机弹性形变对样本集进行扩展，以提高模型的泛化性能．相似手写汉字的识别实验结果表明: 相对于常规的 CNN 模型，文中 CNN 模型的手写汉字识别正确率提高 1．66%，特别是对于变形的手写汉字，识别正确率提高 12．85%; 相对于传统的手写汉字识别方法，文中方法的识别错误率降低 36．47%，从而验证了文中识别方法的有效性．

In order to recognize similar handwritten Chinese characters effectively,a convolutional neural network(CNN) model is proposed,and the topology of the network model is presented.Then,the sample set is extendedby introducing a stochastic elastic deformation to enhance the generalization performance of the model.Experimen-tal results indicate that the recognition accuracy of the proposed CNN model is 1.66% higher than that of the tradi-tional CNN model,especially,for distorted handwritten Chinese characters,the recognition accuracy increases by12.85%; moreover,as compared with the traditional recognition methods,the proposed CNN model reduces therecognition error rate by 36.47%.It is thus concluded that the proposed method is effective.