Shale gas reservoirs are characterized by high compactness and significant heterogeneity, with naturally low production that necessitates hydraulic fracturing technology for enhanced productivity to achieve industrial gas flow. The key to evaluating the effectiveness of fracturing operations and optimizing process parameters lies in obtaining accurate fracture network parameters. Traditional fracture monitoring techniques, such as microseismic monitoring, are costly and cannot achieve full coverage monitoring of well areas. Numerical simulation prediction models require a large number of engineering geological parameters, leading to poor prediction effects for geological data that are incomplete or missing well sections. There is an urgent need for a new method that is economically efficient in obtaining fracture network parameters. To address this, a shale gas reservoir fracture network inversion method based on deep learning was proposed. The core of this method is to quantitatively analyze the fracturing curve characteristic parameters based on the site fracturing curve data, using strongly correlated indicators of fracture network parameters as inputs and microseismic monitoring fracture network parameters (including length, width, height, and volume) as target outputs. A back-propagation (BP) neural network inversion model was established to achieve accurate inversion of fracture network parameters. The model was trained and optimized using 450 fracturing curve segments from shale gas wells in western Chongqing, with the average relative error of fracture network parameter inversion results in the test set being below 15%, which verified the feasibility of this new method for inversion of shale gas reservoir fracture networks.