To address the significant decline in positioning accuracy of traditional algorithms under indoor non line of sight（NLOS）conditions and low beacon deployment density, this paper proposes a fused positioning method based on ranging correction, combining bluetooth low energy（BLE）and pedestrian dead reckoning（PDR）. Firstly, the received signal strength（RSS）of BLE is rapidly constructed using SketchUp indoor 3D modeling software integrated with a ray-tracing algorithm, eliminating the need for tedious manual RSS field collection. Subsequently, a variational autoencoder based on convolutional neural network（VAE-CNN）is designed to predict and correct BLE ranging errors, thereby improving BLE positioning accuracy. Finally, an extended Kalman filter（EKF）is employed to fuse the positioning results from BLE and PDR. Experimental results demonstrate that the proposed ranging-corrected BLE positioning and EKF-based fusion positioning achieve superior performance in environments with NLOS interference and low beacon deployment density.