The assessment of vocational education quality requires precise evaluation of learners’ skills, preparedness, and training outcomes using heterogeneous data sources. Traditional assessment methods, often limited to test scores or completion rates, fail to capture the complexity of individualized learning performance. To overcome these limitations, this research introduces a multimodal feature engineering and multi-model fusion framework for big data-driven vocational education quality assessment. The framework integrates numerical performance data (CSV-based academic and behavioral records) and EEG signal frequency band data (alpha, beta, theta, delta, and gamma brainwaves) to capture both cognitive and behavioral dimensions of learning. Preprocessing steps include Min–Max normalization of numerical data and zero-phase 4th-order Butterworth band-pass noise filtering of EEG signals to ensure high-quality inputs. Feature engineering is performed using Principal Component Analysis (PCA) for dimensionality reduction of normalized numerical features and Wavelet Transform (WT) for time–frequency decomposition of EEG signals. The extracted features are combined using an intermediate-level feature fusion strategy, resulting in a unified multimodal representation. This representation is further processed using an Efficient Migrating Birds Optimized Multimodal Attention-enriched Gated Recurrent Unit (EMB-MAttGRU) model for collaborative skill classification and knowledge extraction across distributed platforms. A fuzzy rule-based system is incorporated to interpret complex learning patterns, while a multi-model fusion strategy with attention-based decision integration enhances predictive accuracy and robustness. Experimental evaluations demonstrate that the proposed framework achieves 99.2% accuracy and 98.2% F1-score compared to conventional assessment models. This integrated approach provides educators, institutions, and policymakers with a comprehensive and intelligent tool for real-time evaluation of vocational education quality, enabling targeted interventions and personalized learner support.