针对目前正交频分复用(OFDM)雷达信号识别方法存在的问题，提出了一种具有可解释性的OFDM雷达信号识别方法。该方法是通过基于树结构的流程优化(TPOT)和与模型无关的局部可理解的解释性(LIME)相结合对OFDM雷达信号进行识别。针对OFDM雷达信号特性提取了复杂度特征和基于时频图矩阵的奇异值熵，组成特征向量；通过TPOT，得到表现最佳的机器学习流程；通过“解释器”解释预测结果，对识别结果做出是否识别正确的风险评估，同时可根据OFDM雷达信号的解释性，得到哪些信号不易区分。实验表明，该方法对信噪比为0 dB时的OFDM雷达信号的识别率达91%，通过LIME给出的解释性可以判断数据集中不易区分的雷达信号类型。

In view of the existing problems in the current Orthogonal Frequency Division Multiplexing (OFDM) radar signal recognition method, this paper proposes an interpretable method for identification of OFDM radar signals. The method which is based on Tree-based Pipeline Optimization Tool(TPOT) and Local Interpretable Model-agnostic Explanations(LIME) is to identify OFDM radar signals. Firstly, according to the characteristics of OFDM radar signals, the complexity features and singular value entropy of time-frequency image matrix are extracted to form the feature vectors. Then through the TPOT，the best performing machine learning process is obtained. Finally，the interpretation result is interpreted by the interpreter, and the result of the recognition is given as a risk assessment; meanwhile，according to the interpretability of OFDM radar signals, those signals difficult to distinguish are determined. The experimental results show that the recognition rate of the OFDM radar signal with RSN=0 dB is 91%. The interpretability given by LIME can be utilized to determine the type of radar signal that is difficult to distinguish in the data set.