Crowd evacuation modeling is evolving from classic physical simulation to data- and behavior-driven intelligent systems， leading to dual trends： continuous improvement of classic models and emergence of new intelligent technologies. However， the existing research lacks an analytical framework that can integrate these two trends systematically and reveal their inherent evolutionary logic， making it difficult for researchers to evaluate comprehensively and select appropriately the existing methods. To address this challenge， a three-stage progressive analytical framework was proposed. In the first stage， the evolution of models driven by multiple factors， including physical environment， emergencies， and subjective psychology， towards higher realism was sorted out. In the second stage， the classic physical models， represented by Social Force Models （SFMs） and Cellular Automata （CA）， and their performance improvements under the integration of multiple factors were evaluated systematically. In the third stage， the cutting-edge intelligent methods were focused on， and the application of Artificial Intelligence （AI） technology in the field of evacuation was deconstructed and induced functionally according to the closed-loop logic of "situation awareness-behavior prediction-decision optimization". This framework reveals the core technological contradiction in this field clearly， that is the trade-off between efficiency and realism， and clarifies the fundamental driving force for the continuous evolution of next-generation intelligent evacuation models. The proposed analytical framework provides a structured， high-level logical view for understanding the complex technology panorama of crowd evacuation modeling， and offers a systematic reference benchmark for researchers in the field when evaluating， selecting， and innovating evacuation models.