The new curriculum reform has been implemented in China for nearly ten years. In the classroom, students take a variety of learning methods; the most important is interactive strategic learning. Deep learning is a process of learning. It emphasizes that learners look at knowledge with critical attitude, actively understand new and old knowledge, and can use the knowledge obtained to solve practical problems. In the face of the development of knowledge explosion, only simple memory, and passive duck -type passive and simple learning methods can no longer meet the needs of modern knowledge. China's current education reform is imperative, but the situation in practice is not satisfactory. Deep learning has achieved many results in search technology, data mining, machine learning, machine translation, natural language processing, multimedia learning, speech, recommendation and personalization technology, education, and other related fields. Therefore, this article has proposed the research of educational models for interactive strategies to promote deep learning in a hybrid environment. In order to make the experimental results more scientific, this article has used deep learning algorithms to analyse it. The results showed that under the same conditions of other conditions, the P values of the three dimensions of Classes A and B were 0.093, 0.172, and 0.317. It was significantly greater than 0.05, and there was no significant difference. When post -testing, the P value of Classes A and B was 0.027, 0.001 and 0.001. It was far less than 0.05, which showed that there was a significant difference in Classes A and B. It indicated that interactive strategies in hybrid environment were conducive to the education model of promoting deep learning, which has proved the positive relationship between the educational model of hybrid interaction and deep learning.

Jin Wan. Research on Educational Model of Deep Learning Promoted by Interactive Strategies in Intelligent Software Environment. Frontiers in Educational Research (2024) Vol. 7, Issue 11: 223-232. https://doi.org/10.25236/FER.2024.071134.

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