The slow real-time and historical data processing speed and insufficient data storage capacity of power grid equipment operation have led to an increase in risk detection time and a decrease in accuracy., A risk detection method for power grid equipment operation based on deep q learning algorithm is proposed for this purpose. Utilize big data mining technology to deeply explore the potential correlation between operational risks and early warning of power grid equipment, and establish a strong correlation model for risk early warning data. Use deep Q-learning algorithm to automatically identify abnormal situations in the operation of power grid equipment and achieve risk detection of power grid equipment operation. The experimental results show that the proposed method can accurately identify and predict potential risks that may occur during the operation of power grid equipment, and greatly shorten the detection response time.