Minimum ignition energy (MIE) is one of the most important and most widely used parameters when characterizing hazardous chemicals. However, it is extremely difficult to obtain experimental MIE data clue to the high cost, time involved, and safety issues of experimental tests. In this work, two quantitative structure property relationship 0.5 (QSPA) models were, built based on existing experimental data and molecular simulations through analysis of multiple linear regression (MLR) and support vector machine (SUM). Experimental MIE data of 61 chemicals were collected, and their molecular descriptors were derived solely from their molecular structures, which were optimized at B3LYP/6310(d) level using Gaussian 09. Both models were validated to have excellent performances in goodness of fit, internal robustness, and external predictive ability, and hence, they are qualified to predict MIE values for Chemicals with no experimental MIE data available. These two validated models can also help gain a better understanding of effects of molecular structures on ignition properties of hydrocarbon fuels.