Existing tree decoder is only suitable for solving single variable problems， but has no good effect of solving multivariate problems. At the same time， most mathematical solvers select truth expression wrongly， which leads to learning deviation occurred in training. Aiming at the above problems， a Graph to Equation Tree （GET） model based on expression level-by-level aggregation and dynamic selection was proposed. Firstly， text semantics was learned through the graph encoder. Then， subexpressions were obtained by aggregating quantities and unknown variables iteratively from bottom of the equation tree layer by layer. Finally， combined with the longest prefix of output expression， truth expression was selected dynamically to minimize the deviation. Experimental results show that the precision of proposed model reaches 83.10% on Math23K dataset， which is 5.70 percentage points higher than that of Graph to Tree （Graph2Tree） model. Therefore， the proposed model can be applied to solution of complex multivariate mathematical problems， and can reduce influence of learning deviation on experimental results.