Multi-physics coupling calculation has applications in many important research fields. If particle transportprocess is included in this calculation, Monte Carlo method is often used to simulate this process and usually alarge amount of calculation time is needed. So, efficient Monte Carlo algorithm for time-dependent particletransport problem is important for an efficiently coupling calculation, which inevitably relies on large-scaleparallel calculation. Based on the characteristic of time-dependent particle transport problem, two methods areproposed in this paper to achieve high- efficiency calculation. One is a tally-reducing algorithm which is used inthe coupling of transport simulation and burnup calculation. By reducing the quantity of data which should bereduced necessarily, this method can reduce the calculation time largely. It can be seen that a new couplingmode for these two processes in MPI environment has a larger value when model scale is larger than the samplesize. The other method is an adaptive method of setting the sample size of Monte Carlo simulation. The law oflarge number assures that the Monte Carlo method will obtain an exact solution when the sample scale tends toinfinity. But generally, no one knows which sample scale is big enough for obtaining a solution with targetprecision in advance. So, the common strategy is to set a huge-enough sample scale by experience and conductthe posterior check for all results. Apparently, this way cannot be efficient because the calculation will go onafter the precision of solution has reached an object value. Another popular method is to set the sample size torely on the relative error of some single calculation. The sample size is enlarged without a break until therelative error is less than some presetting value. This method is not suitable either, because Monte Carloparticle transport simulation will gives feedbacks to other process which is composed of many tallies. It isinappropriate to adjust the sample size according to the relative error of any calculation. Relying on thegeneralization of the Shannon entropy concept and an on-the-fly diagnosis rule for a entropy value sequence, theadaptive method proposed in this paper can reduce the original huge sample scale to a reasonable level. Bynumerically testing some non-trivial examples, both algorithms can reduce the calculation time largely, with theresults kept almost unchanged, so the efficiency is high in these cases
