The identification of cryptographic algorithms is the premise of cryptanalysis which can help recover the keys effectively. This paper focuses on the construction of cryptographic identification classifiers based on residual neural network and feature engineering. We select 6 algorithms including block ciphers and public keys ciphers for experiments. The results show that the accuracy is generally over 90% for each algorithm. Our work has successfully combined deep learning with cryptanalysis, which is also very meaningful for the development of modern cryptography and pattern recognition.