Covariance or its inverse (called precision) matrix estimation is very useful in data analyses. In principle, the empirical covariance matrix calculated with the unlimited samples is an unbiased estimation of the covariance matrix of the distribution from which samples are drawn, but there are only a very limited samples available in many real-life applications. Therefore, \(\ell _1\) -regularized estimation methods are developed in recovering a sparse precision matrix. However, the \(\ell _1\) -regularization has its own drawbacks. To address these drawbacks and the issue of limited samples with multi-classes in biomedical applications, we propose a joint sparse precision matrix estimation method, in which the SCAD-regularization is used for sparsity and the Frobenius norm of the between-class precision matrix difference is adopted to reinforce the similarity among classes. An alternating direction method of multipliers and an iterative weighted penalized method are developed to optimize the objective function. Fisher’s linear discriminant analysis with the estimated precision matrices is applied to two gene expression datasets for lung cancer diagnosis. The diagnosis results indicate the excellent performance of our proposed method for estimating precision matrices.