In order to reduce the manual collection of fingerprint data and obtain high positioning accuracy, a fingerprint database reconstruction and node location of aircraft structure strength test algorithm based on WLAN fingerprint was proposed in this paper. The support vector regression method was used to reconstruct fingerprint data, K-means algorithm was used to reduce the workload of fingerprint collection, and the optimized DBN was used to extract the features of RSS information. Finally, the WLAN fingerprint location database of the aircraft body is established, and the algorithm performance and system were analyzed and evaluated through simulation experiments. The experimental results showed that the average positioning errors of IPDBN-54, IPDBN-41 and IPDBN-26 in KNN algorithm were 10.389 2, 10.786 3 and 11.117 7 respectively. In the WKNN algorithm, the average positioning errors of IPDBN-54, IPDBN-41 and IPDBN-26 were 10.290 4, 10.714 3 and 11.103 8, respectively. The average positioning error of IPDBN was the smallest and the positioning accuracy was relatively high. Compared with BPNN, the average training time of IPDBN was 166.2 s, with relatively low training time. The optimized depth belief network algorithm has strong adaptability to the establishment of WLAN fingerprint location database system, with short training time and high location accuracy. The research aims to achieve accurate spatial positioning of various parts of the aircraft fuselage in the test building and improve efficiency.