As a security measure, CAPTCHA is widely used in Internet. This study proposes a CAPTCHA identification method based on convolutional neural network. Through convolutional layer concatenation, residual learning, global pool, and other technical means, under the premise of ensuring the recognition accuracy rate is not affected, it greatly reduces the amount of network parameters. This study uses the CAPTCHA in the railway ticket website and the CAPTCHA in the educational system as examples to test the performance of the model. For the CAPTCHA in railway ticket website, the experimental results show that this method has the least amount of parameters, and the recognition accuracy of this method is 98.76% for image and the recognition accuracy of the Chinese phrases is 99.14%. For the CAPTCHA in educational system, it has the least amount of parameters and the accuracy is 87.30%.