Standard curves play an important role in the accurate operation of instruments. Today, there are various methods to fit standard curves for instruments. In this paper, the lower-order and higher-order fittings of the microarray scanner standard curve are discussed separately, and we used the maximum relative error as the evaluation index. In the low-order fitting experiments, the Least Squares Algorithm (LSA) could not form an effective constraint on the relative error, which led to the high relative error in its fitting curve in the region of small values, but the Neural Dynamics Optimization Algorithm (NDOA) fitting curve reflected greater advantages in relative error. With the increasing computational power of the hardware, we cannot avoid discussing the higher-order fitting of the data, the NDOA appears to be a poor fit in the higher-order fitting, so we use the LSA (N = 7) with the constraint of minimizing the sum of squares of the errors (SSE) and the Multi-Layer Perceptron (MLP) with the relative error minimization to fit the data at higher order, respectively. Through the higher-order fitting experiments, we find that MLP outperforms LSA (N = 7) in terms of the maximum relative error, and MLP reduces the maximum relative error of the fitted curve to 0.89%. These results will provide some reference value for subsequent standard curve fitting studies of measuring instruments.