针对农业大数据时代数据富集、知识转化难、现有工具数据壁垒高、语义缺失、灵活性不足的问题，本研究设计并开发了农业科学数据在线分析引擎。该引擎采用分层架构，包含用户交互层、智能工作流引擎、知识库与状态管理模块、容器化执行层，核心创新在于：构建数据状态描述符与算子能力画像的元数据驱动机制，提出SC-MPARank混合推荐模型，设计领域语义导向的动态可进化流水线。引擎通过知识图谱实现“持续学习-实时推理”，兼具通用平台灵活性、专家系统专业性与AutoML自动化能力，可智能组织执行现有算法。现已实际应用于育种、耕地、农业绿色发展三大场景，有效降低技术门槛，提升数据到决策的转化效率与可靠性，为智慧农业提供了实用的农业科学数据分析工具。

Aiming at the problems of data enrichment, difficult knowledge transformation, high data barriers of existing tools, missing semantics, and insufficient flexibility in the era of agricultural big data, this study designs and develops an online analysis engine for agricultural scientific data. The engine adopts a layered architecture, including a user interaction layer, an intelligent workflow engine, a knowledge base and state management module, and a containerized execution layer. The core innovations of the engine: constructing a metadata-driven mechanism with data state descriptors and operator capability images, proposing a hybrid recommendation model of SC-MPARank, and designing a dynamic and evolvable pipeline with a domain semantics orientation. The engine achieves ‘continuous learning - real-time inference’ through knowledge graph, combining the flexibility of a general platform, the expertise of an expert system and the automation capability of AutoML, and can intelligently organize the execution of existing algorithms. The engine has been practically applied to three major scenarios of breeding, cultivation, and agricultural green development, effectively reducing the technical threshold, improving the efficiency and reliability of data-to-decision transformation, and providing a practical agricultural scientific data analysis tool for smart agriculture.