The current virtual system for secondary school experiments poses several issues, such as limited methods of operation for students and an inability of the system to comprehend the users’ operational intentions, resulting in a greater operational burden for students and hindering the goal of the experimental practice. However, many traditional multimodal fusion algorithms rely solely on individual modalities for the analysis of users’ experimental intentions, failing to fully utilize the intention information for each modality. To rectify these issues, we present a new multimodal fusion algorithm, MFIRA, which intersects and blends intention probabilities between channels by executing parallel processing of multimodal information at the intention layer. Additionally, we developed an augmented reality (AR) virtual experiment platform based on the Hololens 2, which enables students to conduct experiments using speech, gestures, and vision. Employing the MFIRA algorithm, the system captures users’ experimental intent and navigates or rectifies errors to guide students through their experiments. The experimental results indicate that the MFIRA algorithm boasts a 97.3% accuracy rate in terms of interpreting users’ experimental intent. Compared to existing experimental platforms, this system is considerably more interactive and immersive for students and is highly applicable in secondary school experimental chemistry classrooms.

With the rapid development of public digital cultural resources, the lack of cross-lingual information retrieval (CLIR) services catering to multilingual users in practical applications has created significant language barriers. This hinders the promotion of public digital culture and results in the underutilization of relevant resources. To address this need, this paper constructs M-APE, a shared semantic model that operates without reliance on parallel corpora. Through a three-step process comprising the generation, fine-tuning, and optimization of a shared semantic space, M-APE establishes a common semantic framework for diverse languages. The model utilizes a Chinese semantic space, transferred and trained on authentic public cultural corpora, as its input. Evaluation based on bilingual dictionary induction quality demonstrates that M-APE significantly enhances semantic sharing performance between Chinese and Indo-European languages, represented here by English and French, achieving an average cross-family transformation accuracy of 56.6%. Furthermore, focusing on the CLIR needs of multilingual users within China’s public cultural engineering projects, this study develops a Chinese-English-French cross-lingual information retrieval framework by integrating M-APE into public cultural domain tasks. Experimental results indicate that the proposed method achieves superior cross-lingual retrieval performance in terms of average metrics.