A load detection model based on Event Driven and Deep Learning(EDDL) is proposed to address the issue of low detection accuracy in current non-invasive load detection. The current data is detected through zero crossing, and the key events are discovered from a large amount of data based on event driven mechanisms. The end-to-end non-invasive load detection is achieved by converting the current sequence containing key events into image space and incorporating it into a deep learning based load detection model. The experimental results show that compared with the Multi-class Support Vector Machine(MSVM), Feedforward Neural Network(FNN), Convolution Neural Network(CNN), and Long Short Term Memory (LSTM) models, the proposed EDDL model has better overall performance, with detection accuracy and accuracy of 94.67% and 91.76%, respectively. The simulation results verify that the proposed model can mine current data based on event driven mechanisms and effectively extract current data features based on deep learning models, thus achieving high-precision non-invasive power load detection. This model has certain reference value for the research of non-invasive power load detection.