To enhance the effectiveness of network-based multimedia English education, this study proposes a new teaching model integrating learning motivation theory and constructivism. The model’s design is based on the analysis of statistical features from the parameters of network multimedia English teaching, utilizing learning motivation and constructivism as foundational theories. Additionally, the model incorporates deep learning techniques and intelligent datasets rooted in mathematical logic. By constructing both a network multimedia English teaching dataset and a multimedia teaching resource access dataset, the model applies structural similarity analysis to evaluate these datasets under the influence of learning motivation and constructivism. This paper further investigates the various constraints and objectives faced by students and courses in the context of network-based multimedia English education. The optimization of the teaching algorithm is realized within this framework, aiming to improve efficiency and learner engagement. Simulation results demonstrate that the proposed method offers robust resource matching capabilities with minimal deviation, contributing to enhanced student satisfaction. Moreover, it supports users in the excavation and strategic planning of network-based multimedia English education.