Musical theatre performance integrates emotional expression, character construction, and dramatic development, where singing plays a pivotal role in bridging narrative and music. However, traditional approaches to musical singing and analysis often overlook the contextual and structural nuances embedded in scripts and scores. This study proposes a constructivist-inspired learning and signal processing framework that enhances the accuracy and interpretability of musical theatre singing through deep neural collaborative filtering. Leveraging spectrogram analysis, encoder-decoder architectures, and SA attention-based feature extraction, we construct a multi-module system to improve the fidelity of vocal signal separation and the interpretive quality of performance modeling. Empirical results demonstrate significant gains in sub-module construction accuracy and signal restoration performance, offering a robust technical foundation for intelligent musical analysis.
