The invention discloses a public bicycle flow variation volume forecasting method based on heap model fusion. The public bicycle flow variation volume forecasting method comprises the steps of: 1, adopting a method of fusing public bicycle rental record data and meteorological data for extracting features, and constructing eigenvectors from several perspectives of time, space, meteorology, history, clustering and the like; 2, adopting a distance similarity matrix combining geological positions and a rental relation, clustering by utilizing a clustering algorithm, and configuring clustering features into the eigenvectors; 3, dividing the eigenvectors into five groups according to feature types, training five basic models by utilizing a machine learning system based on a gradient boosting tree algorithm, training features by adopting a cross validation method, and training a heap model by taking results of the five groups of basic models as features. The public bicycle flow variation volume forecasting method based on heap model fusion ensures that a certain difference exists among the basic models, constructs the heap model by adopting the cross validation method finally, improves the accuracy degree of the model, has good forecasting precision, and has small errors.