營建專案需要透過採購發包工程作業項目、機具、設備與材料等專業分包的執行來完成，因此採購優劣會直接影響一個專案在工程專案目標上的成功度，專案工程採購成本高低為影響工程總造價成本的關鍵因素，採購進度則必需配合工程進度來規劃各分包、機具、設備和材料之採購與進場時程，而採購品質也會決定工程施作與材料品質之良窳，因此良好的採購成本、採購時程、採購品質是專案成功之重要因素，故如何研擬影響各工程專案目標之關聯採購績效指標，為探討採購對專案成功度影響之重要課題。
然而目前營建業在採購績效方面的文獻，多偏向公司整體營運的評比，而不是針對營建工程專案管理進行探討，且採購績效對專案成功度的關聯影響研究更是付諸闕如，故本研究針對採購績效建立衡量指標，並確認與專案成功度的關聯影響，透過人工智慧深度機器案例學習(SBiGRU)來預測專案成功度，供管理者以專案目標為基礎，針對採購的價格、時間、品質、數量、職業安全衛生做控管。
本研究以採購績效為基礎之專案成功度預測模式，經與其他人工智慧進行結果比較後，其預測準確率為最佳，證實可提供較準確之專案成功度給管理者進行採購績效的控管。

Construction projects need to be completed through the execution of professional subcontracts such as procurement of outsourcing projects, machinery, equipment and materials. Therefore, the quality of procurement will directly affect the success of a project in terms of project objectives, and the procurement cost of the project will affect the overall project. The key factor of construction cost, the procurement schedule must be coordinated with the project schedule to plan the procurement and entry schedule of each subcontract, machinery, equipment and materials, and the procurement quality will also determine the quality of the project implementation and material quality, so good of the procurement cost, procurement schedule, and procurement quality are important factors for the success of the project. Therefore, how to develop the related procurement performance indicators that affect the objectives of each engineering project is an important topic to explore the impact of procurement on the success of the project.
However, the current literature on procurement performance in the construction industry is more inclined to the evaluation of the overall operation of the company, rather than the discussion on the management of construction projects, and the research on the impact of procurement performance on the success of the project is even more lacking. Therefore, this study aims at Procurement performance establishes measurement indicators, and confirms the impact associated with the project success. Through artificial intelligence deep machine case study (SBiGRU) to predict the project success, managers can use the project goals as the basis to determine the procurement price, time, quality, etc. Quantity, occupational safety and health are controlled.
In this study, the project success prediction model based on procurement performance, after comparing the results with other artificial intelligence, its prediction accuracy is the best, which proves that it can provide more accurate project success for managers to control procurement performance.

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