Accurate prediction of the future trend of the commercial sales volume is of great importance to the development and operation of enterprises and the macro-control by the government. Traditional data prediction methods are time-consuming and subjective, while the existing data-driven future business prediction methods do not take into account the diversity of features in the data sets. The data of the commercial sales volume is time-series, which contains a wealth of time window features, lagging historical features, and price change trend features. Previous studies tend to focus only on some of these features, and the integration and enhancement of these features are seldom explored. The prediction accuracy of the existing future business prediction methods still needs to be improved. Therefore, this study proposes a future business forecast method based on multimodal feature aggregation, which firstly preprocesses the commercial sales volume data and then extracts five different groups of time window features and other features of the data set on the basis of feature engineering. In machine learning, the hard voting mechanism is used to select the appropriate model for the training of the five groups of time window features. At the same time, the neural network optimization model is applied to extract the time-series features and forecast results, and then, the dependency relationships between the data set of the sales volume and some features are analyzed. Finally, with the soft voting model, a high-precision forecast of the commercial sales volume is achieved by complete model integration. The experimental results reveal that the proposed method has high prediction accuracy and efficiency, which is greatly better than the existing prediction methods.