The existing indoor crowd counting methods face the problems limited scenarios, and low detection accuracy, etc. A crowd counting method based on deep neural networks without carrying equipment is proposed in this study. Multiple wireless fidelity (WiFi) sensor nodes are employed to cover indoor areas. The crossover WiFi link data are obtained by detecting signals among sensor nodes. Deep neural network is utilized to learn and extract the features of the effect of the change of the indoor crowd number on WiFi signals. The crowd counting model is trained for the indoor area, and it can be used to estimate the number of crowd by inputting realtime WiFi signals into the model. Evaluation experiments are implemented in a complex indoor office environment. Results show that the proposed method can realize accurate crowd counting with an accuracy of 8223% and the mean error of 037 people. Compared with other machine learning methods, the deep neural network perception model has higher detection accuracy and can be applied to various application scenarios.