Liquid drop model accuracy is optimized 80\% by Bayesian deep neural network (BDNN) to calculate the known nuclei binding energies and also used to predicate extra unknown nucleus. In this paper, KL(Kullback-Leibler) divergence from BDNN is adopted and further optimized by the variational reasoning method. The latest atomic data (AME 2020) is taken as input to train the BDNN, the root means square(RMS) of 2457 types known nuclei($Z\geq 8$ and $N\geq 8$) calculation is improved 80\%(from 2.9894MeV to 0.5695MeV). Additionally, we improved the input of BDNN in this work, so that the unknown nucleus ($Z = 118\sim 126$) can be limited in a region(Regional restriction strategy), which improves the stability of prediction. Experimental data (nuclei $Z=100\sim 117$) also match well with our prediction and showed this calculation method is promising. The further binding energy for proton numbers from $118\sim 126$ is predicated using our method.
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