In the biomedical field, the construction and application of knowledge graphs are becoming increasingly important because they can effectively integrate and manage large amounts of complex medical information. This study provides a whole-process approach for the biomedical field, from constructing knowledge graphs to semantic query based on knowledge graphs. In the knowledge graph construction stage, we propose the BioPLBC model, which incorporates BioBERT context-embedded features, part of speech and lexical morphological features to achieve entity annotation of medical texts. Based on the constructed biomedical knowledge graph, we also propose the Adaptive Locating and Expanding Query (ALEQ) algorithm, which improves the query speed by locating and dynamically expanding the query subregion. The experimental results indicate that the BioPLBC model consistently achieves higher accuracy than the baseline model across all datasets, while the ALEQ algorithm achieves different degrees of improvement in query accuracy and speed.