This research studied and analyzed data from the Labor Force Survey 2009, Office of National Statistics, Thailand. We used a data mining technique to create models for data classification and to study the factors affecting income of the Thai population. The decision tree C4.5 algorithm was used to build models in WEKA. We created the models by using the training data of population in four industries: agriculture and fisheries, manufacturing, trade and services, and other industries. Experiments with four confidence factors (CF) were made to determine the appropriate CF model. We tested the performance of the models using the selected test data based on the correction, precision, recall and the size of the tree, etc. According to our research results, the factor affecting the income of the Thai population in agriculture and fisheries, and trade and services industries is occupation; the factor affecting the manufacturing industry is type of employment, and that affecting other industries is age.