In the digital era, the information value of online prices, characterized by weak price stickiness and high sensitivity to economic shocks, deserves more attention. This paper integrates the high-frequency online inflation rate into the dynamic Nelson-Siegel (DNS) model to explore its relationship with the term structure of interest rates. The empirical results show that the weekly online inflation can significantly predict the yield curve, particularly the slope factor, while the monthly official inflation is predicted by yield curve factors. The mechanism analyses indicate that, due to low price stickiness, online inflation is more responsive to short-term economic conditions and better reflects money market liquidity, thereby having predictive power for the yield curve. Specifically, online inflation for non-durable goods and on weekdays shows stronger predictive power for the slope factor. The heterogeneity in price stickiness across these categories explains the varying impacts on the yield curve.