The invention belongs to the technical field of indoor positioning, particularly an indoor positioning method based on global and local joint constraint transfer learning. According to the method provided by the invention, through minimization of inter-domain marginal and conditional probability distribution discrepancy and maximization of a sample variance in a potential subspace, consistency ofa global structure is constrained. Through minimization of an intraclass variance and maximization of an interclass variance, dependency of each class and corresponding samples is kept. Through manifold regularization, a local neighborhood relationship is kept. Further, the consistency of local structures is constrained. The problem that an existing transfer learning method is insufficient in knowledge transfer can be solved. According to knowledge obtained from a source domain through transfer, positioning precision of a target domain can be effectively improved. The problem of RSS (ReceivedSignal Strength) fluctuation resulting from environment change is solved. The indoor positioning method based on the global and local joint constraint transfer learning provided by the invention is anew high precision positioning method applicable to a complex indoor environment.