The KNN classification algorithm is nonparametric, easy to understand and relatively efficient, and is widely used in many fields. In the traditional KNN algorithm, the Euclidean distance method considers the contribution of all the attributes of the sample as the same. But in fact, the contribution of different attributes of the sample is not necessarily the same. To solve this problem, an improved KNN algorithm based on analytic hierarchy process is proposed. In the improved algorithm, firstly, the weights of each attribute of the sample are calculated by using the analytic hierarchy process, and then the sample distance is calculated by using the weighted Euclidean distance, thereby classifying according to the weighted distance. In the experiment, with the increasing number of training samples, the efficiency of AHP-KNN algorithm is improved, and it is gradually better than the efficiency of the FCD-KNN algorithm and the traditional KNN algorithm. The simulation results show that the improved algorithm proposed can effectively improve the classification accuracy of the traditional KNN algorithm, and has certain theoretical and practical value.