Graphics Algorithm Based Virtual Reality Immersive Interactive Interface Design for International Chinese Language Teaching and Learning

Abstract

This paper addresses the issue of cultural experience deficits in international online Chinese language instruction by proposing a layered optimization method for VR immersive interactive interfaces based on computer graphics algorithms and human visual characteristics. By analyzing human color sensitivity, spatial perception, and multi-channel characteristics, a layered optimization model centered on visual perception intensity is constructed, and a genetic algorithm is employed to solve the layout of interface elements. The experimental section focuses on Chinese language teaching scenarios, determining that the optimal combination is a feature space capacity of 128MB (accuracy rate of 93.18%) and a dictionary space of 512MB (accuracy rate of 78.89%). When the weight parameter is set to 0.35, the recognition accuracy rate reaches 97.23%. Quantitative analysis of 15 teaching elements (e.g., the 3D model of the Hall of Supreme Harmony, cultural explanation subtitles) shows that the core teaching elements (A1/A8) have a visual communication index (VCI) > 3700, and high-intensity visual perception elements (VPI ≥ 4.5) are concentrated in the core teaching area (accounting for 48%). Among the 1,520 element layouts, 32% of high-intensity areas carry 87% of high-VCI elements. The proposed method significantly outperforms existing methods in Chinese cultural image processing, with an average gradient of 6.65–8.05, 1.9–2.8 times higher than contrast methods, a PSNR improvement of 29.7–40.2%, a peak of 21.23, and an MSE of only 2.68%–4.80%, which is 1/7 of contrast methods.