The invention provides a controllable electric heating wind power consumption method based on particle swarm, which constructs an electric heating wind power consumption optimization model by taking the temperature as a characteristic variable, and comprises the steps of A, collecting wind power consumption space-time sequence data; B, establishing a particle swarm wind power consumption system control model; C, establishing a Markov prediction wind power consumption space probability model; D, predicting the wind power consumption space of a certain area in one day by using the prediction wind power consumption space probability model; and E, reasonably consuming the redundant wind power by combining a wind power consumption control model according to the prediction result in the step D.The controllable electric heating wind power consumption method combines the traditional machine learning prediction model, Markov and intelligent algorithm particle swarm, so that the wind power consumption system is enabled to perform calculation and analysis on a relatively accurate result obtained in time sequence judgment prediction in a short term after the combination, and the wind power consumption capability of the system is effectively improved. Meanwhile, the wind power consumption system can consume a large amount of redundant wind power to supply heat to residents, thereby being very applicable to an electric power system in cold areas with a lot of abandoned wind.