The interval concept lattice theory, a new method of mining objects based on interval parameters, can more accurately deal with uncertain information than the classical concept lattice theory. The optimization of interval parameters has been a problem that is not well solved. From the perspective of three-way decision space, we first combine the theories of interval concept lattice and three-way decision and then put forward interval three-way decision space theory; second, in the interval three-way decision space, the positive region, negative region, and boundary region are divided by extension of interval three-way decision concept; further, the decision loss function and three-way decision rules are extracted. Through adjusting interval parameters of the lattice structure, we could find that when parameter is roughly 0.6, more credible decision rules will be mined and decision-making becomes more clear than that under the condition is less than 0.6; finally, we verify the model by a “Green Products Recommendation” example.

Gaussian solitons are important examples in the theory of Riemannian measure space. In the first part, we explicitly characterize the first eigenfunctions of the drift Laplacian in a Gaussian shrinking soliton, which shows that apart from each coordinate function, other first eigenfunctions must involve exponential functions and the so-called error functions. Moreover, the second eigenfunctions are also described. In the second part, we discuss the corresponding issues in Finsler Gaussian shrinking solitons, which is a natural generalization of Gaussian shrinking solitons. For the first eigenfunction, we complement an example to show that if a coordinate function is a first eigenfunction, then the Finsler Gaussian shrinking soliton must be a Euclidean measure space. For the second eigenfunction, we give some necessary and sufficient conditions for these spaces to be Euclidean measure spaces.