Microwave reflectometry will serve as the primary diagnostic tool for measuring plasma density in ITER. The real-time inversion of high spatiotemporal resolution density profiles is a critical research focus for plasma fueling and control in both current and future fusion devices. A fast density profile inversion algorithm based on a deep neural network model has been proposed for the microwave reflectometry profile inversion database under various discharge parameters on EAST. Firstly, the model directly takes the high-sampling-rate raw time-domain data from the multi-bands (Q-, V- and W-bands) microwave reflectometer as input and demonstrates rapid and effective feature extraction capabilities. Secondly, the model outputs a two-dimensional vector containing both the positions of the microwave cut-off layers and the density information. Notably, the model exhibits excellent adaptability across different discharge parameters on EAST, including variations in magnetic field strength ( = 1.57–2.30 T), different plasma confinement such as L-mode and H-mode, and during phases such as current ramp-up and steady-state discharge. The next step involves integrating this data-driven real-time density profile algorithm directly into the deployed profile reflectometer data acquisition system on EAST to enable parallel computation of data acquisition and processing. This integration aims to provide real-time density profile distribution, particularly during density feedback control experiments, such as gas or pellet injection experiments.