Traditional e-commerce review methods face difficulties in identifying implicit evaluation targets and extracting features from both local details and overall semantic levels. To address this, this paper proposes an aspect term extraction model integrating auxiliary sentence construction and a hybrid neural network (EBERT-CBAC). This model first constructs auxiliary sentences to mine potential aspect term information and incorporates part-of-speech (POS) tags, enabling the traditional BERT model to capture multi-dimensional POS word structures. After obtaining contextual embeddings, it uses a CNN layer to extract local contextual features, followed by a BiGRU layer to capture global contextual features within the sentence. Considering that a single attention mechanism cannot capture important sentence features from different perspectives, a multi-head attention mechanism is introduced to process sentence-level information. Finally, sequence labeling is completed in the CRF layer to accurately identify both explicitly expressed and semantically implicit aspect terms in reviews. Experimental results show that, compared to the baseline model BERT-BiLSTM-CRF, the proposed model achieves an average F1-score improvement of 2.48% on three benchmark datasets, effectively enhancing the accuracy of aspect term extraction.