## 2 Related Work  
In the field of information extraction, combining entity recognition and relation classification tasks into a single step can effectively reduce the negative effects of error propagation, strengthen the correlation between the two tasks, and thus improve the overall task extraction efficiency. Currently, there are two primary strategies for merging joint extraction tasks: parameter sharing and sequence labeling [6]. [PERSON] et al. [7] adopted a parameter-sharing approach and proposed an end-to-end model based on long short-term memory networks (LSTM). This model combines the bidirectional LSTM of word sequences with the bidirectional LSTM of dependency syntax trees to capture the dependency information between word sequences and dependency syntax trees, joint model entities and relations in a single model, and realize the joint representation of entities and relations. [PERSON] et al. [8] proposed an RNN model based on an attention mechanism. This model bypasses dependency syntax trees, instead using LSTM and attention mechanisms to directly extract entities and relations from text. [PERSON] et al. [9] proposed a novel joint extraction strategy that transforms entity extraction and relation classification into sequence labeling tasks, providing new ideas for subsequent joint extraction research. [PERSON] et al. [10] proposed an extraction model based on the copy mechanism. The encoder first encodes the text sequence, and a unified decoder or multiple independent decoders are used in the decoding process to extract entities and their relations in the text sequence. [PERSON] et al. [11] developed an extraction model named GraphRel using a graph convolutional network. The model uses linear sequences and dependency structures to extract text features and uses a complete word graph to extract implicit features between word pairs. [PERSON] et al. [12] proposed the CasRel model, which uses a pointer annotation method. First, two binary classifiers are used to identify all subject entities, and then the corresponding object entities and their existing relations are predicted. However, its modeling of prior information in the text is not sufficient, which to a certain extent loses the prediction accuracy of the subject entity, thereby affecting the quality of joint extraction. Based on this consideration, this paper introduces prior information to enhance the modeling of text features to improve the extraction accuracy of the model. As a pictographic language, Chinese contains a large amount of feature information [13], such as Wubi features, glyph features, word boundary features, POS features, and pinyin features. These feature collectively constitute the rich semantic information of Chinese characters. In recent years, the field of Chinese feature fusion has attracted the attention of researchers. [PERSON] et al. [14] proposed the SSP2 Vec method, which generates feature substrings by integrating the strokes, structural features, and pinyin information of Chinese characters, effectively improving the semantic capture ability of word embedding. [PERSON] et al. [15] proposed a named entity recognition model that integrates POS information based on the characteristics of microblog text. The POS information is combined with the word embedding vector and then input into a bidirectional long short-term memory neural network, and the conditional random field is used to decode the output to achieve POS feature-assisted named entity recognition. [PERSON] et al. [16] introduced the pinyin features of Chinese characters and verified through experiments that the introduction of pinyin features can effectively improve the performance of existing models in text recognition. [PERSON] et al. [17] utilized an MECT multivariate metadata embedding cross-transformer to address the problem of the insufficient utilization of Chinese character structural information.