Symbolic music understanding (SMU) is a crucial task in multimedia content understanding, aiming to extract multi-dimensional musical attributes such as melody, dynamics, compositional style, emotion, and genre from symbolic representations. Although existing approaches have substantially advanced dependency modeling in musical sequences, two critical challenges remain: (1) Simplified representation: Current methods typically flatten complex musical structures into linear symbolic sequences, overlooking the inherent multi-dimensional hierarchical information; (2) Lack of music-theory integration: Purely data-driven sequence models struggle to incorporate structured music-theory knowledge, limiting deep semantic understanding of music. To address these issues, this study proposes CNN-Midiformer, a high-precision symbolic music understanding model that integrates structured representations of musical knowledge. First, the model constructs structured representations for music theory and musical sequences based on domain knowledge. Second, a complementary music-feature extraction module is devised to employ convolutional neural networks (CNN) for capturing deep local features from structured musical-knowledge representations, while a Transformer encoder with self-attention captures deep semantic features from musical sequences. Finally, a music-knowledge adaptive-enhancement feature-fusion module dynamically integrates the deep musical-knowledge features extracted by CNN with the deep semantic features of the Transformer via an efficient cross-attention mechanism, thus enhancing contextual sequence understanding and representation learning. Comparative experiments conducted on six public symbolic-music datasets (Pop1K7, ASAP, POP909, Pianist8, EMOPIA, and ADL) demonstrate that CNN-Midiformer surpasses state-of-the-art methods across five benchmark downstream tasks: melody recognition, dynamics prediction, composer classification, emotion classification, and genre classification, achieving a precision gain of 0.21–7.14 percentage points over baseline models.