A new non-invasive measurement method of air gap magnetic density of bilateral permanent magnet synchronous linear motor (BPMSLM)based on the tunneling magnetoresistance(TMR)sensor and the dynamic integrated neural network prediction model based on noise-boosted convolutional neural network(NBCNN),long short-term memory network(LSTM),attention mechanism is proposed. Firstly, the analytical model and the finite element model of air gap magnetic field of linear motor are tormulated as data basis. The nonlinear mapping relationship between external space stray magnetic field and internal central air gap magnetic field of linear motor is explored. Secondly, the TMR sensor is introduced to measure the external stray magnetic field signalof the linear motor,the installation position of the sensor is optimized,and the similarity characteristicsof the internal and external one-dimensional magnetic density signals are matched to obtain the optimal measurement position of the sensor. Then, taking the external straymagnetic field data of the motor as the input and the internal air gap magnetic field data as the output,a high-precision mapping model of the internal and external magnetic fields of NBCNN-LSTM-Attention network is established to realize the non-invasive high-precision measurement of air gap magnetic density. Finally, the experimental platform for measuring the air gap magnetic density of linear motor and the experimental platform for comparative measurement of Gauss meter are built,which verifies the advancement and superiority of the proposed method.