Objective: With the accelerating pace of technological innovation and the growing complexity of policy environments, local governments urgently require intelligent and interpretable decision support systems to guide the strategic layout of future-oriented industries. However, traditional industrial layout approaches rely heavily on expert judgment and static data analysis, making them ineffective at handling dynamic market demands, integrating heterogeneous features, and capturing the complex nonlinear interactions between regional resources and emerging industries. This study aims to address these critical limitations by developing an adaptive deep recommendation algorithm. The proposed algorithm provides data-driven, actionable insights to local policymakers, facilitating the accurate and strategic allocation of regional resources for emerging and strategic industries. Methods: This paper proposes a deep recommendation framework that integrates three key components: feature recalibration, multihead attention mechanisms, and neural matrix factorization. First, diverse regional attributes (such as gross domestic product (GDP), research and development (R&D) expenditure, and infrastructure indicators), industrial characteristics (such as growth rate and technological maturity), and policy orientations (extracted from over 280 local policy documents via semantic embedding) are transformed into dense vector representations through embedding layers. Next, a feature recalibration module inspired by the squeeze-and-excitation network is employed to dynamically reweight critical regional and industrial features, thereby underscoring influential factors and suppressing noise. Subsequently, multihead attention mechanisms are introduced to capture high-order nonlinear interactions across recalibrated features and to model complex interdependencies between regions, industries, and policy orientations effectively. Finally, neural matrix factorization techniques combine collaborative signals with the nonlinear embeddings to score and rank the suitability of each region-industry pairing quantitatively. The proposed model is trained on a comprehensive dataset comprising over 3 000 real-world region-industry samples with a margin-based loss function optimized through supervised learning. Extensive tuning of hyperparameters, including embedding dimensions, dropout rates, and attention head counts, ensures robust model performance. Results: Empirical validation demonstrates that the proposed adaptive deep recommendation algorithm substantially outperforms mainstream baseline models such as DeepFM, AutoInt, and FiBiNet across multiple performance metrics, including logarithmic loss (logloss) and area under the curve (AUC). Specifically, this algorithm achieves the highest AUC score of 0.714 6 and the lowest logloss of 0.485 8 among all tested models, which confirms its superior predictive accuracy and classification capability. Ablation experiments further reveal that each module contributes distinctively to overall performance: Removing the feature recalibration module, multihead attention mechanism, or neural matrix factorization component results in a noticeable degradation of predictive accuracy. Additionally, comparative analysis with a prominent large language model (LLM), such as GPT-4o, highlights the advantage of this structured algorithm in handling numerical and structured data, in contrast to semantic reasoning that limit structural modeling capacities of the LLM. Visualization of attention weights confirms the algorithm's interpretability and explicitly demonstrates its sensitivity to key factors such as regional R&D intensity, infrastructure readiness, and industry technological maturity. Conclusions: This study successfully establishes an adaptive, interpretable, and highly effective deep recommendation algorithm tailored explicitly to future-oriented industrial layout planning. By integrating dynamic feature recalibration, high-order feature interaction modeling, and robust collaborative filtering mechanisms, the proposed algorithm remarkably enhances the accuracy, interpretability, and practical applicability of the recommendations. The proposed algorithm not only provides local policymakers with transparent, data-driven decision support but also sets a theoretical foundation for further exploration into advanced recommendation frameworks. Future research will aim to incorporate temporal dynamics through real-time data streams; expand the framework to multiobjective scenarios covering economic, social, and ecological benefits; integrate structured recommendation outputs with semantic insights from expert knowledge; and ultimately realize a comprehensive, adaptive industrial policy decision support platform.