In engineering practice, information data in the detection process can not be avoided by the working environment and equipment performance and other factors, resulting in unpredictable measurement errors, greatly affecting the reliability of the manufacturing system and decision-making system decision-making scientific. This paper analyzes the application characteristics of untraceable Kalman filter (UKF) and BP neural network algorithm (BPNN) in information data fusion, and tries to realize the complementary advantages of the two through the combination of UKF and BPNN (UKF+BPNN). The information data fusion algorithm based on UKF-BPNN is established to effectively fuse the information data measurement errors brought by multiple influencing factors in order to improve the detection accuracy of the information data, and the feasibility and validity of the method are verified through the application of the algorithm in the measurement of key information data of rolling bearings.