Objective: To analyze the understanding ability of DeepSeek regarding nursing knowledge and explore its potential applications in the nursing field. Methods: A total of 600 nursing practice questions from the People"s Medical Publishing House edition were selected. The question types included 200 single-choice, 200 multiple-choice, and 200 true/false questions, covering subjects such as basic nursing, internal medicine nursing, surgical nursing, obstetrics and gynecology nursing, and pediatric nursing. Instructions were given to DeepSeek to answer the correct option and provide an explanation. The answers from DeepSeek were compared with the reference answers. Questions with disputed answers were excluded, and the final accuracy rate was calculated to evaluate DeepSeek"s understanding of nursing knowledge. Results: Eighteen questions with disputed answers were excluded, and a total of 582 questions were included. The accuracy rates from high to low were as follows: true/false questions (99.46%), single-choice questions (97.95%), and multiple-choice questions (94.68%). The overall accuracy rate for the 582 questions was 97.42%. Conclusion: DeepSeek demonstrated high accuracy in answering the questions, indicating that it can accurately understand and effectively reason nursing-related issues, showing a good understanding of professional knowledge. In the future, it is expected that DeepSeek and other language models can be more widely applied in nursing education, nursing research, clinical decision support, and other fields to improve the efficiency of nursing staff.