Recent advances in device-free wireless sensing (DFS) have shown that it may eventually evolve traditional wireless networks into smart networks which could sense surrounding target location and activity information without equipping the target with any devices. Despite its promising application prospects, one challenging problem to be solved is that the performance of the DFS system degrades significantly in complex scenarios, such as through-wall and non-line-of-sight (NLOS) scenarios. To alleviate this problem, this paper seeks to explore and exploit more informative features from not only the time domain and frequency domain, but also the spatial structural domain. We partition the time domain and frequency domain measurement matrices into basic structure blocks, adopt self-organizing map networks to cluster the blocks into a number of categories, so as to make it feasible to characterize the block distributions. We further adopt coherence histograms to characterize the distribution of the blocks by considering the spatial relationship between adjacent blocks. Thanks to the additional information provided by the spatial structural domain, extensive experimental results achieved in through-wall and NLOS scenarios confirm the outstanding performance of the proposed multi-domain features based DFS system.