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The purpose of this paper is to address the extraction of entities and relationships from unstructured Chinese text, with a particular emphasis on the challenges of Named Entity Recognition (NER) and Relation Extraction (RE). This will be achieved by integrating external lexical information and utilizing the abundant semantic information available in Chinese. We utilize a pipeline model that is applied separately to NER and RE by introducing an innovative NER model that integrates Chinese pinyin, characters, and words to enhance recognition capabilities. Simultaneously, we incorporate information such as entity distance, sentence length, and part-of-speech to improve the performance of relation extraction. We also delve into the interactions among data, models, and inference algorithms to improve learning efficiency in addressing this challenge. In comparison to existing methods, our model has achieved significant results.