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With the development of Internet technology, multimodal data have become the main data resource in the information age. Multimodal learning mode, as an education and teaching mode developed on multimodal data technology, provides more convenience for multimedia teaching. However, many challenges persist in its actual development and application. Currently, the multimodal learning model is susceptible to classroom noise, lack of teaching information, and other factors, which adversely affect multimodal data collection, teaching application, and achievement output. Thus, this paper optimizes the multimodal learning model in the open learning environment and takes 120 engineering students from a university in Guangxi Province as the research object. First, a sequential modal extraction method is proposed by constructing a multimodal probability generation model and then the data are modeled. Semi-supervised learning is then achieved by analyzing and combining the supervised and unsupervised learning processes. Finally, the knowledge base technology with information fusion characteristics is applied to the multimodal teaching mode. This teaching mode has been proven to improve students' learning ability and learning achievement and teachers' teaching effectiveness. [ABSTRACT FROM AUTHOR]