作为材料基因组工程的重要基础，材料数据库的建立及工艺参数优化研究对材料研发具有重要影响。针对目前传统关系型数据库无法较好地存储多源高维异构的合金数据，且材料研发模式亟需从传统的实验试错法转向数据驱动材料研究新模式等问题，提出了基于本体和图数据模型建立合金图数据库，在此基础上，通过改进蜣螂优化算法进行工艺参数组合寻优。利用反向学习初始化种群，再利用可变螺旋搜索策略改进觅食蜣螂位置更新方式，提高算法的全局搜索能力，最后在最优解位置处融合柯西变异扰动和混合策略，产生新解，避免算法陷入局部最优。为了验证所提出的改进蜣螂优化（IDBO）算法的性能，使用23个经典测试函数对IDBO算法进行评估，实验结果表明，IDBO算法性能优于其他算法，显著提高了收敛速度和优化精度。同时，将IDBO算法应用于合金工艺参数优化研究，结果显示IDBO算法性能优于其他优化算法，并得到合金的最佳工艺参数组合，证明了其在工艺参数优化方面的优越性。

As an important foundation of materials genome engineering, the establishment of materials database and the study of process parameter optimization have an important impact on materials research and development. Aiming at the current problems that traditional relational databases can not store multi-source high-dimensional heterogeneous alloy data well, and that the materials R&D mode needs to be urgently shifted from the traditional experimental trial-and-error method to a new mode of data-driven materials research, it is proposed to establish an alloy graph database based on ontology and graph data models, and based on this, to search for the optimal combination of process parameters by improving dung-beetle optimization algorithms. The population is initialized using inverse learning, then the position update method of the foraging dung beetle is improved using the variable spiral search strategy to improve the global search capability of the algorithm, and finally the Kersey's variation perturbation and the hybrid strategy are fused at the optimal solution position to generate a new solution to avoid the algorithm from falling into the local optimum. In order to verify the performance of the proposed Improved Dung Beetle Optimization (IDBO) algorithm, the IDBO algorithm is evaluated using 23 classical test functions, and the experimental results show that the IDBO algorithm outperforms other algorithms and significantly improves the convergence speed and optimization accuracy. Meanwhile, the IDBO algorithm is applied to the study of alloy process parameter optimization, and the results show that the IDBO algorithm outperforms other optimization algorithms and obtains the optimal process parameter combinations of alloys, which proves its superiority in process parameter optimization.