Abstract: As a typical representative of data, time series is widely used in many research fields. The time series anomaly pattern represents the emergence of a special situation, and is of great significance in many fields. Most of the existing time series anomaly pattern recognition algorithms simply detect anomaly subsequences, ignoring the problem of distinguishing the types of anomaly subsequences, and many parameters need to be set manually. In this paper, an anomaly pattern recognition algorithm based on adaptive k nearest neighbor(APAKN) is proposed. Firstly, the adaptive neighbor value k of each subsequence is determined, and an adaptive distance ratio is introduced to calculate the relative density of the subsequence to determine the anomaly score. Then, an adaptive threshold method based on minimum variance is proposed to determine the anomaly threshold and detect all anomaly subsequences. Finally, the anomaly subsequences are clustered, and the obtained cluster centers are anomaly patterns with different changing trends. The whole algorithm process not only solves the density imbalance problem without setting any parameters, but also simplifies the steps of the traditional density-based anomaly subsequence detection algorithm to achieve a good anomaly pattern recognition effect. Experimental results on the 10 data sets of UCR show that the proposed algorithm performs well in detecting anomaly subsequences and clustering anomaly subsequences without setting parameters.