Most aspect level sentiment analysis methods do not focus on keyword features in the local context. Therefore, this study proposes an aspect level sentiment analysis model LCPM (local context pos mask) based on local context keyword feature extraction and enhancement. First, a local context part of the speech mask mechanism is proposed to extract the important words features around aspect words and reduce the interference of noise words. Second, the loss function is modified, so that the model focuses on the local context keyword features related to aspect words and improves the performance of the model’s sentimental classification. Finally, a gating mechanism is designed. The model can dynamically learn the weight coefficients and assign different weight coefficients to local context keyword features and global context features. The experiments on four open datasets show that, compared with existing aspect level sentiment analysis models, the proposed model has higher accuracy and MF1 value, which verifies the effectiveness of local context keyword extraction and enhancement and is of application significance in aspect level sentiment analysis tasks.