and syntax, so it is more difficult to extract semantic features and analyze sentiment for Chinese [1]. Among the commonly used text sentiment analysis methods, the sentiment dictionary method [2] is simple and fast[PERSON] The typically used Chinese sentiment dictionaries include HowNet and the Chinese sentiment vocabulary ontology library of Dalian University of Technology. However, the process of constructing and updating dictionaries requires a lot of manpower and material resources. In addition, the method is separated from the context, unable to understand the different meanings of the same word, and the transferability is weak. In terms of text sentiment analysis methods based on machine learning, the commonly used methods include support vector machines [3], Naive Bayes, and the maximum entropy model [4; 5][PERSON] [PERSON] et al. [6] introduced multiple redundant markers and used conditional random fields to analyze text sentiment. The experimental results are better than the standard model; [PERSON] et al. [7] proposed the XGBoost machine learning algorithm, using a large-scale parallel boosted tree, which is several times faster than common methods, and began to be used on text data [8]; [PERSON] et al. [9] proposed the XGB-CNN model based on a convolutional neural network and XGBoost, and used XGBoost instead of Softmax for regression analysis, which greatly improved the classification accuracy. In general, machine learning is better due to mining text feature modeling, but the model suffers from the problem of sparse semantic features which seriously affects the model's effectiveness. Today, text-based sentiment analysis methods are no longer limited to sentiment dictionaries and machine learning methods. Deep learning and fine-tuning methods based on pre-trained models bring new opportunities to sentiment analysis tasks [10]. Recent studies have shown that they have better generalization ability and stronger migration ability, and constantly refresh the optimal performance of Natural Language Processing (NLP) tasks [11], which can provide reliable technical support for sentiment analysis of subjective texts [12]. In recent years, deep learning methods have been more commonly used in Natural Language Processing. In sentiment analysis tasks, the mainstream methods of deep learning include convolutional Neural Networks (CNNs) [13], Recurrent Neural Networks (RNNs) [14], the attention mechanism model [15; 16], etc. The Long Short-Term Memory (LSTM) [17; 18] network, proposed on the basis of a recurrent neural network, solves the problem of vanishing gradient and exploding gradient to some extent [19]. The Gate Recurrent Unit (GRU) [20] further simplifies LSTM and improves computational efficiency. Later, researchers proposed the Bi-directional Long Short-Term Memory (BiLSTM) [21] and Bi-directional Gated Recurrent Unit (BiGRU), which can relate contextual content and model sequence information in both directions. At the same time, hybrid neural networks complement each other's strengths and bring better results than using a basic model alone [22]. For example, [PERSON] et al. [23] used a model combining CNN and BiLSTM in the sentiment analysis of movie recommendations. CNN was used to extract local features, and BiLSTM was used to obtain the context; [PERSON] et al. [24] proposed a bi-directional CNN-RNN model based on the attention mechanism, which used an independent BiLSTM layer and a BiGRU layer to extract context, and obtained better evaluation results.