The applications of Big Data analytics and Artificial Intelligence (AI) have gained a widespread attention in the construction industry in recent years following the promulgation of Industry 4.0. In the realm of construction research, AI has been utilised widely in areas such as structural design optimization, resource and equipment planning, and project scheduling. The research presented in this paper is aimed to utilise AI to assist with the automatic classification of the large volume of construction material orders created by users through an online marketplace website. Such big data of material orders contained numerous errors (e.g. typographical errors and incorrect units) that were extremely time consuming to correct before the datasets can be used to for further business intelligence analysis. In this research, the dataset was obtained from a business-to-business e-commerce company in Thailand, namely BUILK. The data from BUILK was the construction materials purchase orders created by BUILK's customers through its website, which contained hundreds of thousand unorganized records. In this study, Artificial Neural Networks (ANNs) was applied to automate the categorization of approximately 220,000 records of reinforcement steels orders. The ANNs model was developed and trained using over 32,000 records, with approximately 92 percent of prediction accuracy. The model automatically categorized the steel reinforcement data into 11 groups; Deformed Bars, Round Bars, Wire mesh (Deformed Bars), Wire mesh (Round Bars), Stirrup, Anchor, Material and Others. The outcome of this research helped the company to easily analyze the data to generate insights for its business management and development.