LAUSR

Nursing is an important task in modern medical treatment, which can assist patients in the treatment and rehabilitation process. Nursing practitioners' skills and mentality can affect patient recovery and the speed of treatment. Therefore, there are already a large number of colleges and universities to carry out nursing teaching work. However, the current nursing teaching work still adopts the traditional teaching mode, which is no longer in line with the nursing work of the present era. Nursing teaching not only imparts nursing expertise to students, but it also requires higher practical ability. This study considers the integration of short video technology and text teaching mode into the teaching work of nursing. This study also used the transformer method to extract and predict the characteristics of nursing knowledge, nursing actions, and student satisfaction in short nursing teaching videos and texts. This study also explores the temporal characteristics existing in short videos of nursing teaching. The results show that the T-CNN-L method has higher accuracy than the T-CNN method in predicting the relevant features of nursing teaching short videos. The T-CNN-L method can also accurately and efficiently extract and predict nursing knowledge features and nursing action features.