In the long-term teaching practice, various disciplines have accumulated a large number of teaching resources but cannot function fully and efficiently. For this reason, this study constructs a knowledge mapping of college disciplines based on deep learning. First of all, the overall construction of the atlas is planned, the core concepts of the discipline are identified, the relationships between the knowledge points are defined, and the resources corresponding to the knowledge entities and attributes are expanded. Then deep learning is utilized for the entity construction of the subject knowledge graph, the neural network models BiLSTM+CRF and BiLSTM+Attention are used for the subject entity identification and relationship extraction, and finally the subject knowledge fusion and storage is carried out, and the effectiveness of the designed algorithms is verified on the dataset. The data show that the knowledge representation of knowledge graph is conducive to demonstrating the logical meaning between learning materials, facilitating learners to correlate what they have learned previously with what they are learning now, fusing old and new knowledge, and facilitating learners to meaningfully construct knowledge.