Abstract: The recognition of different driving styles has profound implications for driving safety,road design and fuel economy.Considering that driving styles are affected by drivers’ immediate and back-and-forth operations,this paper proposes a bi-directional LSTM autoencoder-based spectral clustering model for driving style recognition,in order to address the influence of driving data temporality on driving style recognition.Firstly,a whale optimization algorithm is used to select features from the real-time data from the driving process.Secondly,an autoencoder-based bi-directional LSTM model is built to obtain the eigenvalues and eigenvectors for spectral embedding.Finally,the driving styles are recognized by spectral clustering.The analysis of the real-time driving data shows the accuracy of the proposed method is higher than that of SOM and LSTM-based spectral clustering.Besides,the proposed method can effectively identify drivers’driving style and reflect their operating strategies with fewer features.