  The parameters choosing (such as probabilities of choosing X-basis or
Z-basis, intensity of signal state and decoy state, etc.) and system
calibrating will be more challenging when the number of users of a
measurement-device-independent quantum key distribution(MDI-QKD) network
becomes larger. At present, people usually use optimization algorithms to
search the best parameters. This method can find the optimized parameters
accurately but may cost lots of time and hardware resources. It's a big problem
in large scale MDI-QKD network. Here, we present a new method, using Back
Propagation Artificial Neural Network(BPNN) to predict, rather than searching
the optimized parameters. Compared with optimization algorithms, our BPNN is
faster and more lightweight, it can save system resources. Another big problem
brought by large scale MDI-QKD network is system recalibration. BPNN can
support this work in real time, and it only needs to use some discarded data
generated from communication process, rather than require us to add additional
devices or scan the system
