Abstract: The smart grid is gradually being developed into large power cyber physical system, however the interaction between cyber and physical system reduces its ability to withstand the false data injection attack (abbr. FDIA). In allusion to this issue, a method to detect the fake data injection for smart grid based on multi-layer hierarchical fusion fuzzy feature mapping (abbr. MLHFFFM) combined with deep belief network (abbr. DBN) was researched and proposed. Firstly, the principle of FDIA was analyzed and based on MLHFFFM and combining with principal component analysis (abbr. PCA) the smart grid load data was clustered to select the approximate day with daily load similar to that of the forecasted day. Secondly, the conditional DBN was used to analyze the daily grid load of approximate day, and in the meantime, by means of selecting different parameters the daily grid load characteristics was dynamically captured thereby the FDIA was detected. Finally, combining with actual load data of a certain province in China, IEEE 33-bus system was taken for computing example to verify the proposed method. Experimental results show that compared with other models the accuracy rate of the proposed model under different attack intensities keeps above 95%, and the error rate is lower than 5%, thus, the injection of fake data can be effectively detected.