Fingerprint-based Bluetooth positioning is a popular indoor positioning technology. However, the change of indoor environment and Bluetooth anchor locations has significant impact on signal distribution, which will result in the decline of positioning accuracy. The widespread extension of Bluetooth positioning is limited by the need of manual effort to collect the fingerprints with position labels for fingerprint database construction and updating. To address this problem, this paper presents an adaptive fingerprint database updating approach. First, the crowdsourced data including the Bluetooth Received Signal Strength(RSS) sequences and the speed and heading of the pedestrian were recorded. Second, the recorded crowdsourced data were fused by the Kalman Filtering(KF), and then fed into the trajectory validity analysis model with the purpose of assigning the unlabeled RSS data with position labels to generate candidate fingerprints. Third, after enough candidate fingerprints were obtained at each Reference Point(RP), the Density-based Spatial Clustering of Applications with Noise(DBSCAN) approach was conducted on both the original and the candidate fingerprints to filter out the fingerprints which had been identified as the noise, and then the mean of fingerprints in the cluster with the largest data volume was selected as the updated fingerprint of the corresponding RP. Finally, the extensive experimental results show that with the increase of the number of candidate fingerprints and update iterations, the fingerprint-based Bluetooth positioning accuracy can be effectively improved.