In today’s society, engineering cost management and control is very important content. With the increasing number and scale of construction projects in China and huge investment funds, the collection and sorting of all kinds of information data in construction projects has become one of the problems that need to be solved. Therefore, building the construction project cost management and control system is one of the powerful means to improve the work efficiency of processing information and data, which is also very necessary. This paper mainly analyzes the construction of a perfect, efficient, and rapid processing of relevant information workflow based on large database technology to achieve the whole process of project tracking management and effective cost management and control target system. First of all, this paper introduces the construction project cost management, expounds the characteristics of the management system, and studies the application of big data in the construction project. On this basis, the construction project management software is designed and developed. At the same time, the requirements of the system are studied, and the system is tested. The final test results show that the system has a fast processing time and high processing efficiency in processing the above data, which indicates that the construction project cost management and control system based on big data has obvious advantages in processing large-scale data. When the amount of data is small, the processing speed increases exponentially with the increase of nodes. When the amount of data is large, the acceleration ratio is positively correlated with the number of nodes in a certain proportion. With the increase of nodes, the ratio almost remains unchanged, indicating that the system has high operation efficiency and is relatively stable with the increase of nodes.

The whole process management of construction project is to carry out all-round supervision and management of the whole stage of construction project implementation, which can realize the effective allocation of construction project (CP) funds and improve the construction quality at the same time. On this basis, this article analyzes the impact of the whole-process management (WPM) model on the employment ability (EA) of college students (CS), uses computer-aided technology to calculate the information of graduates in a construction company, and analyzes according to the work situation of the graduates in the enterprise and the evaluation of the EA of the graduates by the enterprise cultivation path of university students’ EA. The experimental results of this paper show that the evaluation of the four structural elements of EA of CS by construction companies is higher than that of college students’ self-evaluation, indicating that there is a difference between the EA of students and the needs of enterprise EA, which can be used as an entry point to train students EA.

In order to effectively reduce the risk of construction project (CEP) and maximize the benefit of CEP, this study establishes the construction project economic evaluation (PEE) system by using computer technologies such as transfinite learning machine, artificial intelligence algorithm, and related fuzzy neural network and compares the coupling and sensitivity of different algorithms for PEE indicators, Through the demonstration of the comprehensive efficiency of the PEE system under the condition of computer technology, The results show that the index coupling and early warning sensitivity of the project economic evaluation system using the artificial intelligence algorithm of overlimit learning machine under computer technology are better than the previous traditional algorithms, which can realize the artificial intelligence of the economic evaluation system, reduce the investment cost of the project and improve the delivery time of the project, make the best balance between the technical application and economic operation of the project, and promote the sustainable development of the construction project.