In the process of agricultural water conservancy cost management, capital control belongs to a core content. This paper proposes optimization strategies for the costs incurred in agricultural water conservation, mainly taking agricultural water conservation in agricultural water conservation projects as an example to carry out research. The study uses SPSS software to fit the distribution of the various influencing factors in the unforeseeable costs of agricultural water conservancy projects to analyse and establish an unforeseeable cost rate estimation model and then uses the Monte Carlo method to simulate the unforeseeable factors of agricultural water conservancy project data for the last three years in a city in a certain province A. Finally, the algorithmic performance of the FCM clustering method proposed in this paper is compared with the experiments, and the performance of the algorithms is compared to that of the algorithms according to the FCM. An algorithm to mine cost data for agricultural water conservation projects, study the relationship between project time, cost, and total cost, and propose strategies for optimizing agricultural water conservation. The results show that the algorithm proposed in this paper performs well and meets the requirements followed the unforeseeable cost rate of the agricultural water conservancy project is 5%, which is in line with the actual situation, while the total cost of agricultural water conservancy is roughly in a positive correlation with the cost cost, and the higher the cost cost is, the higher the total cost of the project is.