Facial expression recognition (FER) has widespread application significance in many fields, but it is difficult to extract effective FER features due to local occlusion during the recognition. FER with local occlusion may require expression features of multiple regions, and a single attention mechanism cannot focus on the features of multiple facial regions simultaneously. To this end, this study proposes a local occlusion FER model based on weighted multi-head parallel attention. The model extracts the expression features of multiple facial regions that are not occluded by multiple channels in parallel-spatial attention, alleviating the occlusion interference on expression recognition. A large number of experiments show that the proposed method yields the best performance compared with many advanced methods, and the accuracy on RAF-DB and FERPlus is 89.54% and 89.13%, respectively. On the occluded datasets Occlusion-RAF-DB and Occlusion-FERPlus, the accuracy is 87.47% and 86.28%, respectively. Therefore, this method has strong robustness.