A large number of studies have shown that most vehicle collisions are caused by drivers’ abnormal operations. To ensure the safety of all people on the road network as much as possible, it is crucial to be able to predict the drivers’ driving safety risks in real time. In this paper, we propose a novel cost-sensitive $L1/L2$ -nonnegativity-constrained deep autoencoder network for driving safety risk prediction. Unfortunately, with existing research methods, the size of the sliding time window is too large, the feature extraction is relatively subjective, and class imbalances occur, which leads to low identification accuracy, long prediction times, and poor applicability. We first propose using a three-layer $L1/L2$ -nonnegativity-constrained autoencoder to adaptively search the optimal size of the sliding window and then construct a deep $L1/L2$ -nonnegativity-constrained autoencoder network to automatically extract the hidden features of the driving behaviors. Finally, we build a new $L1/L2$ -nonnegativity-constrained focal loss classifier to predict the driving behaviors under different safety risk levels. The results from the public 100-Car naturalistic driving study dataset indicate that our method can effectively find the optimal window size, reduce the data volume and reconstruction error, and extract more distinctive features. Furthermore, this method effectively curbs the class imbalance, improves the driving safety risk prediction performance, reduces overfitting, shortens the prediction time, and improves the timeliness.