With the deepening of the exploration of informationization in the construction industry, the smart construction site comes into being under the support of technological development and policy. The article combines artificial intelligence technology with electric power smart site, and deeply researches the application of artificial intelligence technology in electric power smart site. For the security monitoring in the smart construction site, a SSD7-FFAM lightweight target detection method is proposed based on the SSD7 algorithm, using feature fusion and attention mechanism methods. Then, based on the fast acquisition of temporal information of surveillance video scenes, an adaptive compression technique with wavelet sparse measurement is designed. Through the model comparison analysis, the SSD7-FFAM algorithm achieves better detection effect and detection speed of 84.97% and 83.45FPS in real application scenarios, and has a smaller number of parameters and computation.The AVCS method can be effectively adaptively adopted, and most of the reconstructed image PSNR values of this method are greater than 40dB under different sampling rates, and the quality of the reconstructed image is better than the Contrast compression technique, which can be used for the high rate compression of intelligent construction site monitoring video. The research results will provide informative ideas for construction companies to introduce AI technology in power smart construction sites.