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E-commerce offers various merchandise for selling and purchasing with frequent transactions and commodity flows. An accurate prediction of customer needs and optimized allocation of goods is required for cost reduction. ,e existing solutions have significant errors and are unsuitable for addressing warehouse needs and allocation. ,at is why businesses cannot respond to customer demands promptly, as they need accurate and reliable demand forecasting.,erefore, this paper proposes spatial feature fusion and grouping strategies based onmultimodal data and builds a neural network predictionmodel for e-commodity demand. ,e designed model extracts order sequence features, consumer emotional features, and facial value features from multimodal data from e-commerce products. ,en, a bidirectional long short-term memory network(BiLSTM-) based grouping strategy is proposed. ,e proposed strategy fully learns the contextual semantics of time series data while reducing the influence of other features on the group’s local features. ,e output features of multimodal data are highly spatially correlated, and this paper employs the spatial dimension fusion strategy for feature fusion. ,is strategy effectively obtains the deep spatial relations among multimodal data by integrating the features of each column in each group across spatial dimensions. Finally, the proposedmodel’s prediction effect is tested using e-commerce dataset. ,e experimental results demonstrate the proposed algorithm’s effectiveness and superiority.

Multimodal Data Guided Spatial Feature Fusion and Grouping Strategy for E-Commerce Commodity Demand Forecasting