This manuscript introduces a bilingual teaching model prediction system called Deep Multi-Scale Fusion Neural Network (DMSFNN). The system utilizes data from the Back-end database, and a pre-processing step is performed to remove noise and imperfect texts using the Adaptive Iterated Guided Filtering method or a global constant score. The English-Chinese Machine Translation (MT) method presented in this research uses Domain-Specific English-Chinese Machine Translation Namib Beetle Optimization-Deep Multi-Scale Fusion Neural Network (DECTMT-NBO-DMSFNN) to address the issues with current translation systems’ frequent and time-consuming mistranslations. The translation process is improved by NBO-DMSFNN after examining the major principles and particular approaches of computer translation from English to Chinese. Pre-processing is done on the English to Chinese translation text data, and DMSFNN quickly extracts and classifies the text's features. The DECTMT-NBO-DMSFNN approach introduces a novel bilingual teaching model for English-Chinese Machine Translation method. Leveraging DMSFNN and NBO, it effectively addresses noise in data through pre-processing, utilizes a Back-end database, and employs feature extraction with the Bilingual Bag-of-Words approach. This unique integration enhances bilingual teaching model prediction and improves English-Chinese MT. Notably, DECTMT-NBO-DMSFNN achieves accuracy level of 99.5% in both languages.

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