The study designs an online educational assistance model based on label distribution using machine learning methods with respect to nursing education practical training courses, through steps such as K-fold basis function determination and CACNN structure design. On this basis, the teaching content, teaching context, and teaching evaluation were comprehensively optimized to promote nursing students’ rapid learning. The fifty nursing students in this study were randomly chosen from Y vocational medical school. The evaluation of their nursing skills, theoretical scores, and other related factors was conducted through independent samples test procedure without considering sample division. This test procedure was carried out in order to observe the feasibility of the nursing education practicum course design strategy suggested in this study and its potential effect on participants. Using the proposed nursing education practicum course design strategy, the participants of the experimental group had much higher average scores compared to those of the control group group. Scores obtained by the experimental group students ranged from 81.68 to 89.12. The practical training skill score difference between the two groups reached 5.64 to 9.12 points in favor of the former group. Furthermore, improvements in critical thinking skills and overall self-efficacy among participants of the experimental group have been recorded. Nursing students recognized the curriculum design strategy of this paper highly, with a comprehensive score of more than 4.
