Pathological diagnosis is the cornerstone for clinical diagnosis and treatment decision-making. The digital intelligence pathology platform built by integrating artificial intelligence, big data, and other information technologies has great application values, which will support the digitalization and intelligent upgrading of the pathology discipline and expand the Chinese solution of intelligent pathology. This study systematically clarifies the conceptual framework of digital intelligence pathology, identifies practical application requirements, and highlights critical challenges in its implementation. Building on proprietary research achievements, we propose a tripartite middleware architecture comprising data, algorithm, and service platforms. The system architecture integrates standardized data management, AI-driven analytical modules, and interoperable service interfaces to optimize pathological workflows. Key workflow improvements include standardized specimen processing, intelligent diagnostic assistance, and platform-based service integration. Furthermore, the study explores prospective application scenarios for digital intelligence pathology platforms, spanning diagnostic services, multidisciplinary consultations, medical education, scientific research, and quality control. Strategic recommendations are provided to accelerate adoption: establishing policy-guided industry standards, diversifying funding channels, strengthening professional training, advancing technological innovation, and ensuring data security with privacy protection. These measures aim to expedite the integration of digital intelligence pathology into clinical practice and support the evolution of smart healthcare.