This paper presents a novel intelligent risk assessment and early warning system for electricity market trading, leveraging historical trading data, weather conditions, and power generation status to predict market risks. As electricity markets become increasingly complex, with factors such as price fluctuations, supply-demand imbalances, and external influences like weather patterns, accurately forecasting these risks is vital for maintaining market stability. The proposed system utilizes machine learning algorithms to process multi-dimensional time-series data and provides real-time risk predictions based on various market scenarios. By enabling proactive risk management, this system helps mitigate potential disruptions, improve market security, and enhance decision-making in the power industry. Our approach offers a significant step toward digitizing and automating risk management in the electricity sector.