Aiming at the problems of data confusion and poor management effect of the technical and economic decision support platform system for power grid infrastructure construction, the planning and design method of the technical and economic decision support platform system for power grid infrastructure construction is proposed. The k-means algorithm is used to optimize the system decision function, build a decision analysis model, and implement the platform system planning and design according to the dynamic changes among multiple entities. Experiments show that the clustering time of this method is less than 1 min, and the technical and economic decision support platform system of power grid infrastructure construction project can effectively screen and manage massive data to ensure the quality of decision.

The current traditional power grid marketing cost forecasting method achieves cost forecasting by studying relevant project examples, which leads to poor forecasting results due to the shallow analysis of cost influencing factors. In this regard, an improved ARIMA model-based power grid marketing cost forecasting method is proposed. The key factors affecting the cost of grid marketing are analyzed by using principal component analysis, and the main indicators affecting the cost are selected by comparing the degree of influence of each indicator on the overall, and the cost prediction model is constructed by using ARIMA algorithm. In experiments, the proposed cost prediction method is validated. The prediction results of the proposed method are in good agreement with the actual cost situation, and the cost prediction performance is better.