The study is aimed at solving the security problems caused by malicious network attacks in human resource management in the development of smart cities. First, the Stackelberg game theory model is used to describe the interaction between intelligent sensors and intelligent jammers, and the security of the information physics system is effectively evaluated. Second, a denoise autoencoder machine model that can be used in human resource management with demographic information is proposed to ensure the security of intelligent sensors. Finally, its performance is simulated and analyzed. The results show that the more packets successfully arrive at the estimator, the more favorable the estimation effect of the estimator is. The designed defense strategy is very effective in protecting the security of intelligent sensors. When the number of nearest neighbor K increases, MAE of four datasets first decreases and then tends to be stable. With the increase of K , MAE of the algorithm proposed decreases from 0.8232 to 0.8095, 0.8086 to 0.7897, and 0.8563 to 0.8351, respectively. It decreases from 1.0169 to 1.0091 and then keeps stable. The recommendation algorithm proposed shows high accuracy in rating prediction and can quickly and effectively extract the hidden features of users and adjust the sparsity of data. Therefore, it can be applied to human resource management. This study provides a new idea for the research on the security of intelligent sensors and human resource management in the development of smart cities.