The invention discloses an unmanned autonomous working platform based on an all-weather unknown environment, and belongs to the field of artificial intelligence and visual navigation. The platform comprises five modules: a stereoscopic vision positioning module, an infrared visible light fusion module, an image recognition module, a map construction module and a loop-back and return detection module. The visual positioning module and the image recognition module share a graph convolutional neural network framework, the visual positioning module selects key frames to perform feature matching and visual positioning, the image recognition module performs semantic classification on a point cloud local map, and the map construction module performs point cloud splicing to form a global depth dense semantic map. The deep neural network is introduced to improve the feature extraction effect and save the extraction time. Monocular vision distance measurement is adopted, so that the multi-parallax registration time is saved. Multi-spectral fusion of key frame images is carried out, all-weather efficient work is achieved, and the detection rate of shielded targets is increased.