The invention discloses a power distribution network investment decision-making method based on deep transfer learning, and the method comprises the steps: collecting and screening data, describing the data distribution characteristics of a
power grid through an edge distribution probability, representing the network relation characteristics of the
power grid through a conditional distribution probability, and completing the characteristic transfer from a source domain
power grid to a target power grid, so that
adaptive learning under a
small sample of power distribution network investment is realized, and finally an input-output nonlinear mapping model based on a target power distribution network is established to make a decision on power distribution network investment. According to the method, a power grid investment input-output association relationship is constructed through a
deep learning network, a power grid investment
decision problem is analyzed from the perspective of pure data, a transfer learning process is introduced, and data distribution characteristics and network relationship characteristics are migrated from other similar power grids through a small number of samples of the transfer learning process by utilizing the self-adaptive characteristic of the transfer learning process, so that the problem that training samples are insufficient in the
association mining process of an existing data driving method is solved.