In recent years, wireless signals using WiFi Channel State Information have played important roles in scenarios such as indoor positioning, fall detection, and identification. However, the impact of multipath effects in complex environments makes the accuracy of fingerprint positioning to be improved. To solve this problem, this paper proposes an improved Density-Based Spatial Clustering of Applications with Noise during the process of noise reduction combined with Enhanced weighted K-nearest neighbor algorithm in the online stage. First, the Hampel algorithm is used to remove outliers of the amplitude information; then, the improved DBSCAN algorithm automatically adjusts the parameter to cluster data; finally, the Enhanced weighted K-nearest neighbor algorithm is used to match the real-time positioning points from the fingerprint database. The experimental results show that the average positioning accuracy of the DBSCAN algorithm reaches 1.579m in a positioning area of about 5×10m2, and the percentage of error within 2m is increased by 42.9% compared to the traditional fingerprint method.