Mining users’ multi-dimensional opinions on products from the complaint texts of new energy vehicles can provide support for product design decisions. Because the complaint text has the characteristics of high entity density and lengthy sentence structure， the existing methods for Aspect-Opinion Pair Extraction （AOPE） suffer from weak correlations between aspect terms and opinion terms. To address this problem， an Aspect-Opinion pair Extraction model based on Context Enhancement （AOE-CE） was proposed， fusing topic features and text features as contextual representation to enhance the correlations between entities. This model was consisted of an entity recognition module and a relation detection module. Firstly， in the entity recognition module， the text was encoded by using a pre-trained model and a part-of-speech tagging tool. Secondly， Bi-directional Long Short-Term Memory （Bi-LSTM） network combined with multi-head attention was employed to capture contextual information and then derive text features. Subsequently， these text features were input into a Conditional Random Field （CRF） model to obtain the entity set. In the relation detection module， the topic features were obtained through BERT （Bidirectional Encoder Representations from Transformers） and fused with the text features to obtain the enhanced contextual representation. Then the tri-affine mechanism was used to enhance the correlations between entities with the help of contextual representation. Finally， the extraction result was obtained by Sigmoid. The experimental results show that the precision， recall， and F1 value of AOE-CE are 2.19， 1.08， and 1.60 percentage points higher than those of SDRN （Synchronous Double-channel Recurrent Network） model respectively， indicating that AOE-CE has better AOPE effect.