Currently, the engineering of miniature spectrometers mainly faces three problems: the mis match between the number of filters at the front en d of the detector and the spectral reconstruction ac curacy; the lack of a stable spectral reconstruction algorithm; and the lack of a spectral reconstruction evaluation method suitable for engineering. Theref ore, based on 20 sets of filters, this paper classifies a nd optimizes the filter array by the K-means algori thm and particle swarm algorithm, and obtains the optimal filter combination under different matrix dimensions. Then, the truncated singular value dec omposition-convex optimization algorithm is used f or high-precision spectral reconstruction, and the d etailed spectral reconstruction process of two typic al target spectra is described. In terms of spectral e valuation, due to the strong randomness of the targ et detected during the working process of the spectr ometer, the standard value of the target spectrum c annot be obtained. Therefore, we adopt the method of joint cross-validation of multiple sets of data for spectral evaluation. The results show that when th e random error of +/− 2 code values is applied mult iple times for reconstruction, the spectral angle cosi ne value between the reconstructed curves becomes more than 0.995, which proves that the spectral rec onstruction under this algorithm has high stability. At the same time, the spectral angle cosine value of the spectral reconstruction curve and the standard curve can reach above 0.99, meaning that it realizes a high-precision spectral reconstruction effect. A hi gh-precision spectral reconstruction algorithm base d on truncated singular value-convex optimization, which is suitable for engineering applications, is est ablished in this paper, providing important scientifi c research value for the engineering application of micro-spectrometers.