Determining sources and spatial distributions of potentially toxic elements (PTEs) is a crucial issue of soil pollution survey. However, uncertainty estimation for source contributions remains lack, and accurate spatial prediction is still challenging. Robust Bayesian multivariate receptor model (RBMRM) was applied to the soil dataset of Qingzhou City (8 PTEs in 429 samples), to calculate source contributions with uncertainties. Multi-task convolutional neural network (MTCNN) was proposed to predict spatial distributions of soil PTEs. RBMRM afforded three sources, consistent with US-EPA positive matrix factorization. Natural source dominated As, Cr, Cu, and Ni contents (78.5%~86.1%), and contributed 37.1%, 61.0%, and 65.9% of Cd, Pb, and Zn, exhibiting low uncertainties with uncertainty index (UI) < 26.7%. Industrial, traffic, and agricultural sources had significant influences on Cd, Pb, and Zn (30.2%~61.9%), with UI < 39.3%. Hg originated dominantly from atmosphere deposition (99.1%), with relatively high uncertainties (UI=87.7%). MTCNN acquired satisfactory accuracies, with R2 of 0.357~0.896 and nRMSE of 0.092-0.366. Spatial distributions of As, Cd, Cr, Cu, Ni, Pb, and Zn were influenced by parent materials. Cd, Hg, Pb, and Zn showed significant hotspot in urban area. This work conducted a new approach exploration, and practical implications for soil pollution regulation were proposed. Environmental implicationSoil pollution arising from potentially toxic elements (PTEs) is a hazard to soil quality, agricultural product safety, and human health, which has been a severe environmental problem around the world. Determining quantitative sources and accurate spatial distributions of PTEs is of great importance for soil pollution assessment and regulation. In this work, robust Bayesian multivariate receptor model was used for source apportionment of soil PTEs with uncertainty estimation, and multi-task convolutional neural network was proposed to predict spatial distributions of soil PTEs. This work is expected to provide a novel approach for source apportionment and spatial prediction of soil PTEs.