Local cattle breeds represent a national strategic animal genetic resource, endowed with irreplaceable value in terms of their distinctive meat quality and flavor, stress resistance, and environmental adaptability. In recent years, the field of digital and intelligent technologies has advanced by leaps and bounds. A new-generation integrated biotechnological breeding system, characterized by artificial intelligence, gene chips, and smart breeding platforms, is reshaping the landscape of beef cattle germplasm innovation. This article provides a comprehensive review of the research and development advancements in domestic intelligent breeding platforms and high-throughput gene chips specifically designed for beef cattle. It places particular emphasis on comparing the core features, parameter configurations, technical strengths, application scenarios, and strategic implications of three types of domestically developed chips tailored for local cattle breeding. Furthermore, it offers a detailed introduction to the establishment and operational status of several pivotal domestic intelligent breeding platforms dedicated to local beef cattle, including the National Beef Cattle Genetic Evaluation Center and Whole Genome Selection Platform, the Huaxi Cattle Intelligent Breeding Management System, the Inner Mongolia BeefNet Precision Breeding and Breeding System Cloud Platform, as well as the Ningxia Beef Cattle Breeding Information Management and Breeding Analysis System. The application of digital intelligence in empowering germplasm innovation technologies for local cattle breeds has yielded significant results. Meanwhile, it further analyzes the practical applications of machine learning and deep learning in key scenarios amid the rapid development of artificial intelligence, including intelligent phenotypic identification of beef cattle, non-destructive meat quality evaluation, optimization of genomic selection models, and full industry chain traceability. Finally, the paper points out the issues of scarcity of high-quality annotated breeding datasets, weak model interpretability, insufficient promotion and application, and difficulty in popularization among small and medium-sized ranches. It suggests solutions such as establishing unified data standards, algorithm interfaces, and computing power centers, developing small sample and federated learning, promoting open source sharing of core algorithms and models, and proposes the vision of building a unified and open intelligent local cattle breeding cloud ecosystem in the future, thereby providing a reference for the high-quality and sustainable development of China's local cattle breeding industry.