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Two reduced simulation approaches are exploited to predict the parametric boundary of dominant instability regime with global effects and the characteristics of corresponding turbulent particle fluxes in tokamak plasmas. One is usual numerical simulation of coexisting ion temperature gradient (ITG) mode and trapped electron mode (TEM) turbulence employing an extended fluid code (ExFC) based on the so-called Landau–Fluid model including the trapped electron dynamics. Here the density gradient (i.e. R/L n ) driven TEM (∇n-TEM) is emphasized. The other one is a surrogate turbulence transport model, taking a neural network (NN) based approach with speeding calculation. It is shown that the turbulent particle flux, particularly their directions depend on the type of micro-instability as ITG and/or TEM. On the other hand, the density gradient may govern the direction of the turbulent particle fluxes in general circumstances. Specifically, in the parameter regime explored here, the ITG and the electron temperature gradient driven TEM (∇T e-TEM) are destabilized for flat density profile, generally causing an inward particle flux, i.e. particle pinch. Contrarily, for steep density profile, the ∇n-TEM or coexisting ITG and TEM turbulence are dominant so that the particle always diffuses outwards. An empirical criterion is obtained to predict the dominant instability and the direction of particle flux for medium density gradients, involving the gradients of both ion and electron temperature as well as the density. These two transport models are applied to analyze the spontaneous excitation of a quasi-coherent mode in the turbulence modulation discharge by MHD magnetic island observed on tokamak HL-2A, clearly showing a dynamic transition from ITG to TEM. Furthermore, the ExFC-NN model can predict and speed up the analysis of the turbulence transport in tokamak experiments.