In this paper, a novel machine learning based approach is proposed for automated cost analysis on priced bill of quantities prepared by tenders in the construction industry. The proposed approach features: 1) An effective integration of structured project-specific information with surveyor's domain knowledge in order to model the complex interrelationships between the specifications and descriptions of an item and its trade category; 2) An effective transformation by supervised t-SNE to map the original data into a 2-dimensional space to tackle issues of high dimensionality in modelling and creating classifiers, and 3) Simple classifiers with a high classification accuracy and a good generalization capability. Relevant comparative experimental results have demonstrated the effectiveness of the proposed approach.