With the proliferation of Artificial Intelligence （AI） computing power to the edge of the network and even to terminal devices， the computing power network of end-edge-supercloud collaboration has become the best computing solution. The emerging new opportunities have spawned the deep integration between end-edge-supercloud computing and the network. However， the complete development of the integrated system is unsolved， including adaptability， flexibility， and valuability. Therefore， a computing power network for ubiquitous AI named ACPN was proposed with the assistance of blockchain. In ACPN， the end-edge-supercloud collaboration provides infrastructure for the framework， and the computing power resource pool formed by the infrastructure provides safe and reliable computing power for the users， the network satisfies users’ demands by scheduling resources， and the neural network and execution platform in the framework provide interfaces for AI task execution. At the same time， the blockchain guarantees the reliability of resource transaction and encourage more computing power contributors to join the platform. This framework provides adaptability for users of computing power network， flexibility for resource scheduling of networking computing power， and valuability for computing power providers. A clear description of this new computing power network architecture was given through a case.

Most of the existing methods to compute text similarity based on Vector Space Model (VSM) use TF-IDF scores as the weights of feature terms in text, which ignores the word sense relationships among feature terms and lead to inaccurate text similarity. To improve the accuracy of text similarities calculated by methods based on VSM, a new term weight computing method by integrating word sense was proposed in this paper. Firstly, word sense similarities among feature terms were computed based on the Chinese WordNet. And then, the TF-IDF weights were revised according to the word sense similarities for the purpose of reflecting both the frequency and the word sense of feature terms in text. The experimental results on the HIT IR-lab Multi-Document Summarization Corpus show that to use the weights calculated by the proposed method can efficiently improve the differentiation among document clusters.