This study focuses on electricity price forecasting issues under different factors. Against the backdrop of the global energy transition, electricity prices are affected by various factors such as energy, meteorology, market, and time, which renders traditional forecasting methods limited. It elaborates on the influence mechanisms of different factors on electricity prices: for instance, in the energy scenario, there are differences in the impacts of renewable energy and traditional energy on electricity prices; in the meteorological scenario, factors like temperature exert dual influences on electricity prices. Meanwhile, this paper analyzes the evolutionary path of electricity price forecasting models, ranging from statistical models and machine learning to deep learning, discusses current challenges including the quantification of nonlinear correlations, generalization ability in extreme scenarios, and model interpretability, and proposes future research directions such as dynamic scenario weight allocation, multi-scale data fusion, and lightweight model design.