Abstract: With the rapid development of service computing, cloud computing, internet of things, e-commerce, and modern logistics industry, cross-domain logistics services cooperation has become the main development trend of the modern logistics industry. The dynamic optimal composition of web services in logistics has become the key technology to create large and powerful logistics services based on the available logistics services of different companies that achieve seamless convergence of logistics services, satisfy user complex requirements, and realize the value addition. Recently, owing to the technologies of web services, cloud computing, and service sciences, an increasing number of logistics companies have registered themselves as logistics web service providers. The logistics services composition should satisfy the user's global QoS constraints and provide the best quality of service (QoS.) Currently, with the rapid development of cloud computing, e-commerce, service computing, and modern logistics industry, many logistics services are available on the network providing similar functions and different levels of QoS. These factors make the problem of determining the optimal composition of a logistics service a typical Np-hard problem. This study proposes a method to achieve the dynamic optimal composition of domain QoS and resource-aware logistics services and to realize logistics services that are dynamic, offer quality of domain services, and are aware of resource requirements. First, the learning artificial bee colony algorithm (LABC) is proposed; LABC is applied to decompose the global QoS constraints into local QoS constraints that logistics task nodes must satisfy and to transform the global optimization problem of logistics service composition into a local optimal service selection problem. Second, during the process of logistics service process execution, for each task node, the logistics service with best domain QoS evaluation, which can satisfy the local QoS constraints and resource requirements, is chosen to achieve a high-quality dynamic logistics service and optimal composition of service. The results of simulation experiments show that the proposed method is feasible and effective.