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.