Research on the Design of Interactive Art Teaching System for iOS System

The lack of interactive guidance in the art self-learning process is currently the main problem that learners face, which often makes it difficult to continue learning. To solve this problem, the study designs an interactive art teaching system. This system is based on the iOS system and is primarily aimed at art beginners. The instructional system and learners participate in educational exchanges using the iOS painting image style classification model. Further, the system realizes interaction between students through the painting description generation model. This process facilitates intra-platform interaction. The results shows that the classification accuracy of the designed painting image style classification model is above 80.00%, and the average recall rate is 78.16%. In addition, the designed painting description generation models are accurate in the range of 84.45% to 97.74%, and the recall rate is in the range of 83.71% to 98.27%. The study demonstrates the effectiveness of the interactive art teaching system for providing guidance to beginner in the art department.