Abstract: From the completion of 3D model design for parts to the selection of suitable machining enterprises, the rapid and accurate matching of manufacturing resources with manufacturing demands has become a critical challenge. To efficiently bridge the design and manufacturing processes for machined parts, a machine tool resource matching and optimization model for collaborative machining of multi-feature parts is proposed. Processing task units are generated based on extended data exchange and atomic task similarity analysis. Machine tool resources are encapsulated into service entities through ontology modeling, and candidate machine tool resource sets for each task unit are determined based on machining capability matching. Subsequently, quality of service (QoS) metrics from service scheduling are introduced to establish a comprehensive evaluation index system for optimal resource combination. A multi-objective particle swarm optimization (MOPSO) algorithm is employed to solve the model. Experimental results demonstrate that the proposed method effectively improves the efficiency of resource matching and combination optimization. In the obtained Pareto front solutions, the highest comprehensive evaluation score reaches 0.8518, and the minimum total transfer distance is 628 km, achieving enhanced efficiency in machine tool resource integration and allocation.