This paper presents an indoor user location method based on machine learning. The circular neural network algorithm is used to construct the building three-dimensional raster association model. Then

combined with MR data of indoor users

to complete the indoor user location track fitting and location positioning. On this basis

KQI sensing data on the correlation signaling side can realize intelligent monitoring of user perception. A pilot project was carried out in a certain city to investigate and confirm the identified user perception problems on the spot

and the location positioning accuracy reached 88.65%. After two months of special rectification

327 user perception problems were solved

and the room coverage rate of MR increased from 94.41% to 97.22%.