To prevent the occurrence of elevator safety accidents, in this study, an Internet of things-based elevator failure monitoring system is investigated. First, it introduces the Internet of things technology, preprocesses the relevant data, and extracts the features of the elevator operation data continuously collected by the elevator sensors. The objective of this paper is to study the application of IoT in elevators. Method. The Relief-F algorithm is used to evaluate the potential influencing factors. The results show that, as the batch size increases, the accuracy rate will gradually increase, but after more than 20, the accuracy rate will decrease. When the batch size is 20, the training result is the best. It can be seen that, as the time step increases, the accuracy of the prediction will be significantly improved. When the time step is 24, the prediction accuracy is the highest; after 24, the prediction rate will decline. In the diagram of the influence of learning rate on the model, the blue line indicates that the learning rate is 1.0, the red line is 0.1, and the black line is 0.01. With the increase of the iteration times, the effect is the best when the learning rate is 0.1. This system has high research value and broad application prospects and can make up for the current lack of monitoring in the elevator industry.