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Abstract: The minimum variational free energy criterion comprises two criteria: the maximum semantic information criterion and the maximum information efficiency criterion, but it does not provide a method for balancing them. The Semantic Information G Theory, the author proposed in his early years, extends the rate-distortion function R(D) to the rate-fidelity function R(G), where R is the minimum mutual information for given semantic mutual information G. Semantic Variational Bayes (SVB) is based on the parameter solution of R(G), where the variational and iterative methods originated from Shannon et al.'s research on the rate-distortion function. SVB not only uses likelihood functions but also truth, membership, similarity, distortion, and copula density functions as constraint functions. It explicitly uses the maximum information efficiency (G/R) criterion and facilitates the trade-off between maximum semantic information and maximum information efficiency. The computational experiments include 1) using some mixture models as an examples to show that mixture models converges as G/R increases; 2) demonstrating the application of SVB in data compression with a group of error ranges as the constraint; 3) illustrating how the semantic information measure and SVB can be used for maximum entropy control and reinforcement learning in control tasks with given range constraints, providing numerical evidence for balancing control's purposiveness and efficiency. The limitation of SVB is that it does not account for parameter probability distributions. Further research is needed to apply SVB to deep learning.