Translation analysis of black earth cultural communication using multimedia artificial intelligence

With the deepening of global cultural exchange, translation quality has become increasingly important in cultural communication.Traditional machine translation methods often struggle to meet expectations, especially when dealing with multimodal content such as video subtitles, audio narration, and text embedded in images related to Heitu culture.These conventional approaches frequently fail to handle long-range dependencies and complex semantic structures.This study leverages multimedia artificial intelligence to combine the sequence modelling capabilities of long short-term memory (LSTM) networks with the global information capture of Transformer models.This integration improves the handling of long-range dependencies and semantic consistency across heterogeneous data sources.When applied to complex multimedia materials related to Heitu culture, the proposed method demonstrates significantly higher translation quality.Through the introduction of a reinforcement learning mechanism, the proposed model can dynamically adjust translation strategies when processing culturally specific terminology and emotional nuances, thereby enhancing cultural adaptability in translations.