Simulation of Dynamic Evolution of Identity in Cross-Cultural Music Learning

As advanced electromagnetic sensing and intelligent signal processing systems increasingly rely on robust feature extraction and multimodal information fusion, modeling heterogeneous cultural information provides useful insights for cross-domain representation learning. This study constructs a hybrid neural network model based on a multi-layer perceptron and self-organizing mapping to simulate the dynamic evolution of identity when individuals encounter heterogeneous musical cultures. The model couples three modules—music feature extraction, cultural distance perception, and self-concept adjustment—while integrating the parallel internalization of musical structures and the symbolic logic of patterns as coupled cognitive processes. Through 500 simulation cycles, the proposed framework reproduces the nonlinear trajectory of identity evolution and identifies three representative stages: cognitive accumulation during initial contact, identity structure reorganization during deep immersion, and pluralistic identity integration. Simulation results reveal an inverted U-shaped relationship between cultural adaptability and learning depth, where moderate exposure promotes integration whereas excessive intensity may trigger emotional ambivalence and identity instability. Sensitivity analysis over 100 parameter sets further demonstrates that cultural openness and music feature extraction accuracy determine statistically significant bifurcation points (p < 0.01). The proposed computational framework provides a quantitative perspective for modeling dynamic identity evolution and offers methodological references for multimodal feature learning and intelligent information processing in complex heterogeneous environments.