Sensors are important components in intelligent detection and automation devices. To solve the problem of fusion under multisensor asynchronous data, an innovative sliding clustering-based multi-sensor asynchronous information fusion method is proposed. Firstly, a K-Means clustering method is introduced to tolerate the asynchronous problem, which mainly uses curve fitting to give a simple and fast rule of thumb for the calculation of k-values in the real-time clustering method. Secondly, a clustering filter kernel is designed to form a sliding pipeline for fusion in the spatial-temporal domain. In this way, the variation of data is always kept within an acceptable error, and the real-time multi-sensor information fusion method is fully implemented. Finally, the experiments validate the correctness and rationality of the designed clustering fusion method. The experiments show that the SC-MSIF method is correct and feasible and has a better performance in terms of real-time performance, and the RMSE error of the SC-MSIF method is reduced by 47. 8% and 36. 3% compared to the EKF and MEAN methods. The actual test results of multi-sensor fusion in UAVs are also better.