Aiming at the problem that most of existing models for document-level sentiment analysis only consider encoding text at word level， an attention sentiment analysis model based on multi-scale convolution and gating mechanism was proposed. Firstly， in order to obtain more different levels of text semantic information and form a richer text representation， the multi-scale convolution was used to capture local correlations of different granularities. Secondly， considering the influence of user personality and product information on text sentiment classification， the global information of users and products was integrated into attention to capture key semantic components which were highly related to users and products to form the document representation. Thirdly， a gating mechanism was introduced to control the path of emotional information flowing to collection layer. Finally， the sentiment classification was realized through the fully connected layer and argmax function. The experimental results show that， compared with the baseline model with the most advanced performance， the proposed algorithm has the sentiment classification accuracy on IMDB and Yelp2014 datasets improved by 1.2 percentage points and 0.7 percentage points respectively， and obtains the smallest Root Mean Squared Error （RMSE） on IMDB and Yelp2013 datasets.