Due to the development of deep learning technology, an increasing number of researchers tend to use deep neural network to learn text feature representation for sentiment analysis, where sequence models and graph neural networks have been widely used and achieved good results. However, for aspect based sentiment analysis tasks, there is a long-distance dependency between aspect objects and other words. Although the sequential neural network can capture the contextual semantic information of sentences, the long-distance dependency between words cannot be effectively learned. Graph neural networks can aggregate more aspect-dependent information through graph structures, while ignoring contextual semantic relationships between ordered words. Thus an aspect-based sentiment analysis model named BiG-AN (bi-guide attention network) is proposed. The model combines the advantages of bi-directional long short-term memory (BiLSTM) and graph convolution network (GCN) to capture sentiment features at the aspect level of text, using interactively guiding attention mechanism to focus on contextual and long-distance dependency information in the sentence. The experimental results on four open-source datasets, including Laptop, Rest14, Rest16 and Twitter, show that the proposed model can extract richer aspect-based text features and effectively improve the results of aspect based sentiment classification compared with other benchmark models.