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To address the problem of radar High-Resolution Range Profile (HRRP) target recognition, traditional methods only consider the envelope information of the sample and ignore the temporal correlation between the range cells. In this study, we propose a bidirectional self-recurrent neural network model based on an attention mechanism. The model divides the HRRP data in the time domain into two sequences, i.e., forward and backward using a sliding window, then extracts the features through two independent GRU networks, and splices the extracted features simultaneously, thus utilizing the bidirectional temporal information of HRRP. Considering that sequences at different moments have different degrees of importance to the target classification, different attention weights are assigned to the hidden layer features at each moment. Finally, the model uses the hidden features weighted summation to obtain target recognition and classification result. Experimental results show that the proposed method can effectively solve the target recognition problem of HRRP, and that the target area can still be accurately identified when the time shift occurs.