## 1 Introduction  
Extracting entities and relations from unstructured text is a fundamental task in natural language processing, which is typically divided into two subtasks: named entity recognition (NER) and relation extraction. There are two main approaches in which a pipelined approach is employed to perform named entity recognition and relation extraction on text separately. The other is to model the two tasks jointly. In this study, we use the pipeline model to model a named entity recognition model and a relationship extraction model, respectively, and achieve good results on the relevant datasets, respectively. Information extraction is an important part of the natural language processing task, which is the process of converting unstructured textual information into valuable and organized structured information. Among them, Named Entity Recognition (NER), Entity Relationship Extraction (RE), and Event Extraction are the main tasks of information extraction. Named entity recognition is the basis of complex NLP tasks, which aim to identify entity elements in a text and categorize them into pre-defined entity classes [1]. In general, named entity recognition can be implemented in three different ways: rule-based methods, statistical machine learning-based methods, and deep learning-based methods [2]. Entity relationship extraction is currently used more often based on deep learning models. Deep learning is currently more successful for two reasons: on the one hand neural networks can memorize the trained model, and on the other hand deep learning can further discover deep features [3]. The essence is to migrate the weights of a multilayer network to other networks so that other networks can learn the features of this network. Currently, research in Chinese named entity recognition predominantly focuses on character-based approaches. However, when dealing with intricate entity relationships, the task of Chinese relation extraction confronts formidable challenges. Currently, research on Chinese named entity recognition and relation extraction primarily focuses on character-based or word-based approaches. However, when dealing with intricate entity relationships, the Chinese entity relation extraction task faces significant challenges. How to introduce vocabulary beyond entity characters:  
The semantics contained in Chinese sentences are composed of words, meaning that Chinese words contain a vast amount of information [4]. If word embeddings are learned through large unlabeled text corpora, the impact of encountering unknown words in the text corpus will be significantly reduced. Introducing external vocabulary further helps to better address the issue of unclear lexical boundaries. How to better utilize the rich semantic information in Chinese:  
In the field of deep learning, models based on characters, lexical properties, sounds, etc., are extensively studied to improve the accuracy and generalization of relation extraction. Fully leveraging semantic information aids in a better understanding of relationships between entities. Consistency of symmetry with data distribution:  
In academic discussions, ensuring the consistency of symmetry within the triad of data, model, and inference algorithm with the symmetry of data distribution is crucial for achieving optimal learning efficiency. Addressing this issue requires in-depth exploration of the relationships between these symmetries and the development of more flexible models to enhance learning efficacy. The main contributions of our study are outlined as follows:  
1. Introduction of a novel NER model combining Chinese pinyin, characters, and words to enhance recognition capabilities;
2. The fusion of entity distances, sentence lengths, and part of speech improves the performance of the relation extraction model;
3. Exploration of the interaction between data, models, and inference algorithms to investigate synergies and symmetry in deep learning, resulting in enhanced learning efficiency.