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Abstract: High-precision indoor positioning offers valuable information support for various services such as patient monitoring, equipment scheduling management, and laboratory safety. An traditional indoor positioning technology, fingerprint indoor positioning, often employs the K-nearest neighbors (KNN) algorithm to identify the closest K reference points (RPs) via received signal strength (RSS) for location prediction. However, RSS is susceptible to environmental interference, leading to the selection of RPs that are not physically the closest to the user. Moreover, using a fixed K value is not the optimal strategy. In this work, we propose a novel approach, the dynamic K-nearest neighbor method based on strong access point (AP) credibility (SAPC-DKNN), for indoor positioning. In SAPC-DKNN, we leverage prior knowledge of RSS path loss and employ the RSS fluctuation area to quantify the significance of different APs. We integrate the similarity of AP sets within the range of strong APs and formulate a weighted distance metric for RSS based on the credibility of strong APs. Additionally, we introduce a dynamic K value algorithm (ND-DKA) based on neighbor density for the automatic optimization of the K value for each test point. Experimental evaluations conducted on three datasets demonstrate that our method significantly reduces the average positioning error by 18.67%-66.48% compared to the state-of-the-art KNN methods.