The identification of urban functional zones can assist in the decision-making process in urban construction. This paper proposes a multi-source scene feature fusion method utilizing the Transformer model for urban functional zone identification. Firstly， the Traffic Analysis Zone （TAZ） is constructed based on the road network. The graph structure of POI （Point of Interest） data is created using the Delaunay Triangulation （DT）. Additionally， remote sensing data is utilized to obtain the corresponding image objects for each TAZ. Subsequently， the POI graph structure is processed using a Graph Convolution Network （GCN） to extract social scene features. Meanwhile， the natural scene features of remote sensing data are obtained through encoding with ResNet-50. Finally， the multi-head attention mechanism of Transformer decoder is utilized to fuse multi-dimensional feature vectors， facilitating accurate identification of urban functional zone with SoftMax. Taking the main urban area of Shenyang as an example， multi-source data such as OSM （Open Street Map）， POI and remote sensing data in 2021 are used as experimental data. The results indicate that the overall accuracy and Kappa coefficient of this method are 82.2% and 70% respectively. Furthermore， the Kappa coefficient is at least 18% higher than that of the single data method and at least 9% higher than that of other fusion methods. This study innovatively employs the Transformer model to integrate social and natural scene features， effectively addresses the challenge of combining diverse features from multiple sources into an integrated representation， and provides a new technical approach for urban functional zones identification.