When WiFi fingerprint positioning technology utilizes existing access points for WiFi fingerprint positioning, there are problems such as uncontrollable AP quality, redundant fingerprint databases, and large real-time positioning calculation volume. To solve these problems, a Hybrid Robust Access Point Selection method is proposed. This algorithm first analyzes the stability of APs through comprehensive signal stability index in the offline stage, and then analyzes the similarity of APs using mutual information and correlation coefficient to screen out stable APs with low similarity to construct a new lightweight fingerprint database. In the online stage, the log-distance path loss model is used to evaluate the real-time signal quality and select high-quality APs for matching positioning. Experimental results show that compared with the original database, this algorithm effectively eliminates redundant APs, reduces positioning error by 57.79%, and increases the probability of positioning accuracy below 1.5 m from 68.75% to 93.75%.