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This invention relates to the field of display control technology, and specifically discloses a display device brightness adaptive control method based on multimodal environmental perception and deep reinforcement learning. The method includes: real-time acquisition of multi-dimensional environmental perception data and interaction data to generate scene feature identifier vectors; cluster analysis of historical brightness adjustment records of the display device to obtain feature threshold vectors for each usage scenario, and identifying the current scenario; retrieving and executing the initial brightness decision strategy for the current scenario from the policy library while collecting device and user feedback data; constructing a comprehensive reward formula by combining the standard differentiated weights of multiple control objectives in the current scenario with a multi-objective reward formula; calculating the comprehensive reward value of the initial strategy based on the feedback data and the comprehensive reward formula; iteratively updating the brightness decision strategy based on the comprehensive reward value using a priority experience replay mechanism, correcting the scene's baseline brightness or weights, and achieving deep reinforcement learning-driven brightness adaptive control; enabling the display device to adaptively adjust its brightness based on deep reinforcement learning.