This study evaluates the understanding of business management concepts among nursing staff for the elderly in Japan. Text data comprising 325 answers and 3,080 sentences from 26 participants’ reports collected from October to December 2024 were analyzed. Morphological analysis and deep learning models (T5(Text-to-Text Transfer Transformer) and BERT (Bidirectional Encoder Representations from Transformers)) were used to classify two groups by scores, achieving high accuracy and F1 scores. The difference between the two groups was whether the seminar participants merely understood the lecture or were trying to take it back to the workplace and put it into action. By analyzing the reports submitted by seminar participants, using Topic Modeling with NMF (Non-Negative Matrix Factorization) and BERTopic, the key themes were identified as strategic planning, community welfare, organizational philosophy, and actionable insights. By applying Topic Modeling to the seminar participants' LINE conversations, it was found that the LINE data captured more practical and personal concerns, such as scheduling, punctuality, and emotional impressions. This study demonstrated the potential for automated evaluation of training programs and aligning participant engagement with organizational goals.