Sentiment analysis， as a subdivision of Natural Language Processing（NLP）， has experienced the development of using sentiment lexicon， machine learning and deep learning to analyze. According to the problem of low accuracy， over fitting phenomenon in training process and low coverage， large workload when compiling the sentiment lexicon when using the generalized deep learning model as a text classifier to analysis of Web text reviews in a specific field， a sentiment analysis model based on sentiment lexicon and stacked residual Bidirectional Long Short-Term Memory （Bi-LSTM） network was proposed. Firstly， the sentiment words in the sentiment lexicon were designed to cover the professional words in the research field of "educational robot"， thereby making up for the lack of accuracy of Bi-LSTM model in analyzing such texts. Then， Bi-LSTM and SnowNLP were used to reduce the volume of compilation of the sentiment lexicon. The memory gate and forget gate structures of Long Short-Term Memory （LSTM） network were able to ensure that the relevance of the words before and after in the comment text were fully considered with some analyzed words selected to be forgotten at the same time， thereby avoiding the problem of gradient explosion during the back propagation. After the introduction of the stacked residual Bi-LSTM， not only the number of layers of the model was deepened to 8， but also the "degradation" problem caused by the residual network stacking LSTM was avoided. Finally， by setting and adjusting the score weights of the two parts appropriately， and the sigmoid activation function was used to normalize the total score to the interval of ［0，1］. According to the interval division of ［0，0.5］ and （0.5，1］， negative and positive emotions were represented respectively， and sentiment classification was completed. Experimental results show that the sentiment classification accuracy of the proposed classification model for the reviews dataset about "educational robot" is improved by about 4.5 percentage points compared with the standard LSTM model and by about 2.0 percentage points compared with the BERT （Bidirectional Encoder Representation from Transformers）. In conclusion， the sentiment classification model based on sentiment lexicon and deep learning classification model was generalized by the proposed model， and by modifying the sentiment words in the lexicon and appropriately adjusting the layer number and the structure of the deep learning model， the proposed model can be applied to accurate sentiment analysis of shopping reviews of all kinds of goods in e-commerce platform， thereby helping enterprises to understand the consumers’ shopping psychology and the market demand， as well as providing consumers with a reference standard for the quality of goods.