The invention discloses a laser scanning SLAM indoor three-dimensional point cloud quality evaluation method based on deep learning. The method comprises the steps that S1, acquiring high-quality point cloud through a laser scanning SLAM device; S2, performing degradation on the high-quality point cloud to obtain a simulation point cloud; S3, carrying out track measurement analysis on the simulation point cloud; S4, extracting a plane from the high-quality point cloud and the simulation point cloud, performing local consistency noise analysis and geometric rule analysis on the plane, and quantifying the quality of the point cloud; S5, segmenting the high-quality point cloud and the simulation point cloud to obtain point cloud blocks; S6, normalizing the point cloud blocks and then inputting into a Point Net + + neural network for model training, and obtaining a network model; S7, performing point cloud quality analysis on the point cloud to be evaluated through the step S4 to obtain a point cloud quality level value; and S8, predicting the to-be-evaluated point cloud through the neural network model obtained in the step S6, and judging whether the point cloud belongs to a high-quality point cloud or a quality-reduced point cloud. The invention provides a method for quantifying the quality of point cloud, and establishes a classification standard and a framework for evaluating an indoor three-dimensional point cloud model under an SLAM system.