The Transformer model can learn important information in the input sequence, which shows higher accuracy compared to the traditional automatic speech recognition (ASR) model. The Conformer model adds a convolution module to the Transformer’s encoder, which increases the ability to obtain subtle local information and further improves the performance of the model. In this study, the Conformer model and the N-gram language model (LM) are used in combination for Chinese speech recognition, and a good recognition effect is obtained. Experiments on the data sets of AISHELL-1 and aidatatang_200zh show that the character error rate of the Conformer model can be reduced to 5.79% and 5.60%, respectively, which is 5.82% and 2.71% lower than that of the Transformer model. Upon the combination with the N-gram LM, the character error rate can be reduced to the optimal performance of 4.86% and 5.10%, respectively, and the real-time factor (RTF) can reach 0.14566. When the test signal-to-noise ratio is reduced to 20 dB, the character error rate of the model drops to 8.58%, which indicates the anti-noise ability of the model.