Distribution networks have complex topologies and a limited number of monitoring devices. Because quick and accurate fault location is critical for the operation quality and reliability of distribution systems, a lot of methods have been proposed, including the installation of additional monitoring equipment. Although accurate fault location is possible by dividing the complex topologies into simple ones with fewer branches, the cost of installing additional devices is uneconomic, which makes it hard to be widely applied in practical systems. Based on the theoretical analysis of the relationship between electrical measurements and fault points, an intelligent location method with limited measurement information for multibranch distribution networks is proposed. Logical reasoning and machine learning (ML) are combined to locate fault points with limited measurement information. The relationship between the fault point and unknown distribution of uncontrollable factors in fault equivalents is fit by the ML model which can preserve time-related features of electrical waveforms. An adaptive migration strategy by transfer learning is proposed to deal with the problem of small samples in practical application. The proposed method is tested in different topologies. Simulation results prove the effectiveness and generalization capability of the proposed method under complex operating conditions.