Code comments plays an important role in software quality assurance, which can improve the readability of source code and make it easier to understand, reuse, and maintain. However, for various reasons, sometimes developers do not add the necessary comments, which make developers always waste a lot of time understanding the source code and greatly reduces the efficiency of software maintenance. In recent years, lots of work using machine learning to automatically generate corresponding comments for the source code. These methods extract such information as code sequence and structure, and then utilize sequence to sequence (seq2seq) neural model to generate the corresponding comments, which have achieved sound results. However, Hybrid-DeepCom, the state-of-the-art code comment generation model, is still deficient in two aspects. The first is that it may break the code structure during preprocessing, resulting in inconsistent input information of different instances, making the model learning effect poor; the second is that due to the limitations of the seq2seq model, it is not able to generate out-of-vocabulary word (OOV word) in the comment. For example, variable names, method names, and other identifiers that appear very infrequently in the source code are usually OOV words, without them, comments would be difficult to be understood. In order to solve this problem, the automatic comment generation model named CodePtr is proposed in this study. On the one hand, a complete source code encoder is added to solve the problem of code structure being broken; on the other hand, the pointer-generator network module is introduced to realize the automatic switch between the generated word mode and the copy word mode in each step of decoding, especially when encountering the identifier with few times in the input, the model can directly copy it to the output, so as to solve the problem of not being able to generate OOV word. Finally, this study compares the CodePtr and Hybrid-DeepCom models through experiments on large data sets. The results show that when the size of the vocabulary is 30 000, CodePtr is increased by 6% on average in translation performance metrics, and the effect of OOV word processing is improved by nearly 50%, which fully demonstrates the effectiveness of CodePtr model.