The traditional knearest neighbor (kNN) algorithm has been widely used in data classification, but its redundant distance computation consumes much time for highdimensional data processing. Meanwhile, for the classification algorithms based on landmarkbased spectral clustering (LCkNN and RCkNN), the nearest neighbor points of the current testing point are partially missing, which results in low accuracy. Aiming at the above problems, we propose a clusteringbased annular knearest neighbor algorithm (AkNN). Based on the traditional clustering algorithm, the proposed algorithm collects data points with high similarity in the training set into a cluster. Then an annular filter centered on the testing point is set. Finally, the points in the filter are classified by the kNN algorithm. We can choose a clustering algorithm freely according to practical needs. The performance of the proposed algorithm is tested on 6 open data sets in the UCI database. Experimental results show that the AkNNE and AkNNH algorithms reduce calculation by 51% on average compared with the kNN algorithm, and enhance the accuracy by 3% on average compared with the LC-kNN and RC-kNN algorithms. In addition, the proposed algorithm is still valid in 10000 dimensional datasets.