In recent years, formal methods have been adopted to detect the hardware Trojans (HT). However, they generally suffer from the time-consuming and error-prone development for property, lack of self-learning system to counter with the future HT types, and high computational complexity due to the growth of design scales. To overcome the above limitations, we propose an automatic security property generation method (ASPG) by feature analysis and property matching techniques. Machine learning is applied to systematically training the property library from the suspicious behaviors in unknown designs, which is expected to counter with the future HT. To reduce the computational complexity, we transform the register-transfer level (RTL) code into an introduced succinct abstract format to remove the redundant information which is unnecessary for depicting HT features. Experimental results show that the properties are generated in less than 50 ms with low memory consumption and the benchmarks from Trust-hub and DeTrust can be successfully detected with 0 false negatives and positives.