Phononic crystals offer valuable sensing capabilities due to their high sensitivity to changes in sound velocity of analytes. In this work, a reconfigurable inverse design approach of phononic crystal sensors is achieved using a deep learning accelerated evolution strategy. The training data is acquired through finite element method (FEM). Two multilayer perceptrons (MLP) are constructed and trained to predict the center frequency and bandwidth of a passband in the dispersion relation. Utilizing a two-step training approach enables rapid accuracy enhancements and swift reconstruction of network targets from NaCl to KCl solutions. The trained networks accelerate the optimization process, yielding a phononic crystal with good detection ability. Compared to FEM, invoking the trained networks can reduce optimization time by a factor of 105. The optimized and initial structures are both fabricated and experimentally tested. The robust linear relation between the resonant peak and the solution concentration indicates significant sensing value. The experimental results are in good agreement with the FEM simulations. In the detection of NaCl solution, the optimized phononic crystal sensor has a sensitivity increase of 375% and a Q-factor of 999%. Our research demonstrates that the data-driven deep learning network is a very powerful tool for the design and optimization of phononic crystal devices.