The Aspect-Based Sentiment Analysis （ABSA） task aims to identify the sentiment polarity of a specific aspect. However， the existing related models lack the short-distance constraints on the context of the aspect word for the natural sentences with uncertain structure， and easily ignore the syntactic relations， so it is difficult to accurately determine the sentiment polarity of the aspect. Aiming at the above problems， an ABSA model with Embedding Different Neighborhood Representations （EDNR） was proposed. In this model， on the basis of obtaining the word order information of sentences， the nearest neighbor strategy combining with Convolution Neural Network （CNN） was used to obtain aspect neighborhood information， so as to reduce the influence of far irrelevant information on the model. At the same time， the grammatical information of sentences was introduced to increase the dependency between words. After fusing the two features， Mask and attention mechanism were used to pay special attention to the aspect information and reduce the interference of useless information to the sentiment analysis model. Besides， in order to evaluate the influence degree of contextual and grammatical information on sentiment polarity， an information evaluation coefficient was proposed. Experiments were carried out on five public datasets， and the results show that compared with the sentiment analysis model AGCN-MAX （Aggregated Graph Convolutional Network-MAX）， the EDNR model has the accuracy and F1 score on dataset 14Lap improved by 2.47 percentage points and 2.83 percentage points respectively. It can be seen that the EDNR model can effectively capture emotional features and improve the classification performance.