In this work, a purely data-driven discharge prediction model was developed and tested without integrating any data or results from simulations. The model was developed based on the experimental data from the Experimental Advanced Superconducting Tokamak (EAST) campaign 2010–2020 discharges and can predict the actual plasma current Ip, normalized beta βn, toroidal beta βt, beta poloidal βp, electron density ne, stored energy Wmhd, loop voltage Vloop, elongation at plasma boundary κ, internal inductance li, q at magnetic axis q0, and q at 95% flux surface q95. The average similarities of all the selected key diagnostic signals between prediction results and the experimental data are greater than 90%, except for the Vloop and q0. Before a tokamak experiment, the values of actuator signals are set in the discharge proposal stage, with the model allowing to check the consistency of expected diagnostic signals. The model can give the estimated values of the diagnostic signals to check the reasonableness of the tokamak experimental proposal.