This study combines the technical progress of the DeepSeek large model with global digital trends. It systematically analyzes its innovative practices in technical architecture, industry applications, and ecosystem building. Using DeepSeek’s strong knowledge processing capabilities, we build a three-tier interconnected financial knowledge graph (“company - industry - macro”) and add a policy transmission path sub-graph. This helps achieve in-depth decoupling of multi-scale features. We adopt an innovative “DeepSeek-MoE + Temporal Convolutional Network (TCN)” hybrid architecture. It is specially optimized for the multi-scale features of financial data. Focusing on the financial securities field, we creatively build a multi-scale quantitative stock selection model based on DeepSeek. To address the multi-scale features of financial market data, we introduce innovations such as adaptive Hurst thresholds, policy event fusion factors, and high-frequency attention layers. These effectively overcome the shortcomings of traditional quantitative models in handling high-frequency noise and low-frequency trends. Empirical results show that this strategy achieves an annualized return of 29.3% and a maximum drawdown of 16.8%. It significantly outperforms traditional models and single-scale strategies. It provides a more adaptive and accurate new paradigm for quantitative investment decisions.