Aspect-level sentiment classification task, which aims to determine the sentiment polarity of a given aspect, has attracted increasing attention due to its broad applications. The key to this task is to identify contextual descriptions relevant to the given aspect and predict the aspect-related sentiment orientation of the author according to the context. Statistically, it is found that close to 30% of reviews convey a clear sentiment orientation without any explicit sentiment description of the given aspect, which is called implicit sentiment expression. Recent attention mechanism-based neural network methods have gained great achievement in sentiment analysis. However, this kind of method can only capture explicit aspect-related sentiment descriptions but fails to effectively explore and analyze implicit sentiment, and it often models aspect words and sentence contexts separately, which makes the expression of aspect words lack contextual semantics. To solve the above two problems, this study proposes an aspect-level sentiment classification method that integrates local aspect information and global sentence context information and improves the classification performance of the model by curriculum learning according to different classification difficulties of implicit and explicit sentiment sentences. Experimental results show that the proposed method not only has a high accuracy in identifying the aspect-related sentiment orientation of explicit sentiment sentences but also can effectively learn the sentiment categories of implicit sentiment sentences.