This article proposes a neural network (NN)-based calibration framework via quantization code reconstruction to address the critical limitation of multidimensional NNs (MDNNs) in analog-to-digital converter (ADC) calibration, where performance degrades drastically under varying input frequencies or sampling rates. Conventional MDNN methods suffer from distribution shifts caused by dynamic oversampling ratio (OSR) variations, necessitating repeated retraining. Our innovation lies in a data reconstruction mechanism: for lower OSR scenarios, a third-order cascaded integrator-comb (CIC) filter inserts pseudo-data points between quantization code to match the NN’s input dimension. For higher OSR scenarios, cyclic downsampling decomposes the original sequence into parallel subsequences processed independently by the same network. For multitone signals, a reconstruction–calibration–summation flow hierarchically handles spectral components. Implemented on an FPGA and covalidated with a commercial 14-bit/1 GSPS pipeline ADC, the framework demonstrates robust performance without retraining. Experimental results show: bandwidth expansion from 16.3% to 84.7% of the Nyquist bandwidth. SFDR improvement of 13.8–27.7 dB and ENOB gain of 2.3–4.6 bits across 10.3–160.3 MHz inputs. This work enables consistent high-precision ADC calibration in dynamic signal environments, facilitating deployment in complex application scenarios.