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With the rapid development of smart city and 5C, user demand for Internet services has increased exponentially. Through collaborative content sharing, the storage limitation of a single edge server (ES) can be broken. However, when mobile users need to download the whole content through multiple regions, independently deciding the caching content for ESs in different regions may result in redundant caching. Furthermore, frequent switching of communication connection during user movement also causes retransmission delay. As a revolutionary approach in the artificial intelligence field, deep reinforcement learning (DRL) has earned great success in solving high-dimensional and network resource management related problems. Therefore, we integrate collaborative caching and DRL to build an intelligent edge caching framework, so as to realize collaborative caching between cloud and ESs. In this caching framework, a fed-erated-machine-learning-based user behavior prediction model is first designed to characterize the content preference and movement trajectory of mobile users. Next, to achieve efficient resource aggregation of ESs, a user-behavior-aware dynamic collaborative caching domain (DCCD) construction and management mechanism is devised to divide ESs into clusters, select cluster heads, and set the re-clustering rules. Then a DRL-based content caching and delivery algorithm is presented to decide the caching content of ESs in a DCCD from a global perspective and plan the transmission path for users, which reduces redundant content and transmission delay. Especially when a user request cannot be satisfied by the current DCCD, a cross-domain content delivery strategy is presented to allow ESs in other DCCDs to provide and forward content to the user, avoiding the traffic pressure and delay caused by requesting services from cloud. The simulation results show that the proposed collaborative caching framework can improve user satisfaction in terms of content hit rate and service delay. [ABSTRACT FROM AUTHOR]