Aiming at the problems of deep learning-based multi-scale edge detection inevitably low adaptability, increased parameters, large calculations, and discontinuous detection edges, this paper proposes a multi-scale edge detection method based on improved overall nesting. The method combines multi-scale detection with weak supervision model to solve the problem of large amount of parameter calculation. In order to make full use of the powerful feature expression ability of convolution, based on the whole nested edge detection, a multi-scale deep learning structure is proposed, which is an independent multi-network multi-scale structure, which is composed of multiple networks with different depths and outputs.At the same time, the whole nested weight mixing layer is referenced. The weight mixing layer connects all the weak supervised prediction results together and learns the mixed weight in the training process. The performance of the proposed method is evaluated by the evaluation index on the data set BSDS500. Experimental results show that the proposed method can achieve good performance on BSDS500.