<p>This paper proposes an innovative CPO-VMD-PConv-Informer framework to forecast the KBW Nasdaq Financial Technology Index (KFTX). The framework comprehensively incorporates the effects of eight representative uncertainty indicators on KFTX price forecasting, including the Economic Policy Uncertainty Index (EPU) and the Geopolitical Risk Index (GPR). The empirical findings are as follows: (1) The proposed CPO-VMD-PConv-Informer framework demonstrates superior forecasting performance across the entire sample period, achieving R² values of 0.9681 and 0.9757, significantly outperforming other commonly used traditional machine learning and deep learning models. (2) By integrating VMD decomposition and CPO optimization, the model effectively enhances its adaptability to extreme market volatility, maintaining stable forecasting accuracy even under structural shocks such as the COVID-19 outbreak in 2020. (3) SHAP analysis demonstrates that TM constituted the predominant determinant influencing FinTech index forecasting results during the pre-COVID-19 period, while FSI emerged as the principal driving force in the post-pandemic era, effectively supplanting TM's previously dominant predictive role. (4) Robustness tests show that the proposed model consistently delivers strong forecasting performance across different training–testing data splits (9:1, 8:2, and 6:4), with the MAPE remaining below 2%. These findings provide methodological advancements for forecasting in the KFTX market, offering both theoretical value and practical significance.</p>