Firstly, for the problem of floating positioning coordinates of the tag nodes, after analyzing and processing the collected positioning data, it is found that the positioningdata collected each time when the tag nodes are stationary is different, and there are certain errors, which leads to position coordinates to float. From the perspective of thetag nodes motion state classification, the motion state is judged by using a machine learning classification algorithm, which the acceleration and the difference in distance arespecified as the model feature for model training. The resulting classification algorithm model can judge the motion state well. According to the determined motion state, it isfurther determined whether the position coordinates of the current tag node need to be recalculated, thereby solving the problem of floating position coordinates, and improvingthe stability and accuracy of the indoor positioning system.Secondly, to solve the problem that the indoor positioning system has no solution or misinterpretation due to abnormal positioning data, after preprocessing the collectedpositioning data, the analysis found that it belongs to time series data. From the perspective of prediction processing of positioning data, through the use of time seriesprediction models and machine learning prediction algorithms, the prediction processing of abnormal positioning data is performed to avoid the failure of positioning. By dividingthe collected positioning data into data sequences, training the prediction model, and analyzing the prediction effects of the prediction model, it is found that the positioning data prediction process can solve the problem of no solution or misinterpretation, and further improve the stability and accuracy of the indoor positioning system.Finally, according to the solutions of the above two problems, the integrated processing of the 3D indoor positioning algorithm is performed. By using the UWB-Based asynchronous TDOA positioning model, the clock synchronization problem faced by the indoor positioning system is successfully solved, and the system complexity is reduced to a certain extent. Then, by using the UWB wireless communication method based on TDMA, each tag node in the system is successfully allocated time slots to allocatecommunication resources, thereby avoiding the problem of data collision, and realizing real-time positioning of multi-label nodes.In this paper, the three-dimensional indoor positioning test system actually constructed is tested. The test results verify that the three-dimensional indoor positioning algorithm has high positioning accuracy and stability.