In the task of multi-target stance detection, there are problems the mutual influence of content describing different targets, resulting in reduction in accuracy. To solve this problem, a multi-target stance detection algorithm based on a bidirectional long short-term memory ( Bi-LSTM) network with position-weight is proposed. First, the corresponding position of the target in the input text is calcu-lated with the ultimate position-weight vector. Next, the position information and output from the Bi-LSTM layer are fused by the position-weight fusion layer. Finally, the stances of different targets are predicted using the LSTM network and softmax classification. The multi-target stance detection cor-pus of the American election in 2016 is used to validate the proposed method. The results demon-strate that the Bi-LSTM network with position-weight achieves an advantage of 1 . 4％ in macro aver-age F1 value in the comparison of recent algorithms.