The present invention provides a large-scale high-speed rotary equipment measuring and neural network learning regulation and control method and device based on rigidity vector space projection maximization, belonging to the technical field of mechanical assembly. The method utilizes an envelope filter principle, a two-dimensional point set S, a least square method and a learning neural network to realize large-scale high-speed rotary equipment measuring and regulation and control. The device comprises a base, an air flotation shaft system, an aligning and tilt regulating workbench, precise force sensors, a static balance measuring platform, a left upright column, a right upright column, a left lower transverse measuring rod, a left lower telescopic inductive sensor, a left upper transverse measuring rod, a left upper telescopic inductive sensor, a right lower transverse measuring rod, a right lower lever type inductive sensor, a right upper transverse measuring rod and a right upper lever type inductive sensor. The method and the device can perform effective measuring and accurate regulation and control on large-scale high-speed rotary equipment.