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In the anomaly recognition of animal body temperature, methods such as infrared temperature measurement are prone to system bias, making the results unreliable. Deep learning based anomaly detection algorithms has poor robustness and generalization performance on different temperature measurement devices, and is difficult to apply to non-standardized temperature measurement scenarios with low data volume, strong randomness, and inconsistent standards. Therefore, a method of animal body temperature anomaly recognition for non-normalized data sources was proposed. The abnormal animal body temperature detection could be completed by measuring the similarity between body temperature time series data. An improved dynamic time warping (iDTW) algorithm was proposed to solve the problem that the commonly used similarity measurement algorithms were not effective in sequence matching and sequence distance measurement. The Euclidean distance and the first derivative were integrated in the measurement between data points, which effectively solved the problem of sequence over-alignment. The sequence intersection ratio was used to represent the overall characteristics of the sequence, which improved the effect of sequence distance measurement. Aiming at the problem of anomaly detection of unequal length sequence based on similarity measure, an anomaly detection method based on sliding window and sequence equal division was proposed. The shorter sequence was used as the sliding window to traverse the longer sequence to obtain a set of sequence distance. According to the different stages of training and detection, the maximum or the minimum value was selected as the similarity measurement result to solve the problem of unequal length sequence matching. To solve the problem of excessive distance between normal samples and undetected anomaly caused by the long sequence, the long data sequence was equally divided into multiple sub-sequences, and the sum of the sub-sequence distance would be taken as the final similarity measurement result. Experimental results on the public dataset UCR showed that the iDTW algorithm outperformed Euclidean distance, dynamic time warping, derivative dynamic time warping and weighted dynamic time warping by an average of 6.0, 3.0, 5.2 and 2.5 percentage points on 10 time series datasets, respectively. Compared with the classical anomaly detection algorithms, the F1 score of the anomaly detection method based on sliding window and sequence equal division on three animal body temperature datasets were increased obviously.