With the advancement and development of society, the research and application indecision-making methods have greatly improved. But knowledge economy andinformation society coming today, the decision problems we face are more and morecomplicated. They have some characteristics such as: the coexistence of enormousinformation and small sample information; the coexistence of certainty and uncertainty;the coexistence of veracity and inveracity; the coexistence of dynamic quality and staticquality; the coexistence of using models and not using model; the coexistence of singletarget and multi-targets ;the coexistence of linearity and nonlinearity etc. The existingdecision-making methods universally have many problems when confronted with thecomplex decision problems mentioned above, such as: difficult to identify complicatedinformation; difficult to construct models; difficult to choose functions and parameters;complicated to compute (NP problems appears easily) etc. For example, traditionaldecision-making methods easily bring about the difficulty of modeling(especially indealing with multi-factors and multi-targets decision problems), modern soft computingdecision-making methods(such as neural net decision, genetic arithmetic etc)are proneto induce the difficulty of choosing functions and parameters and lead to compute morecomplicated (NP problems appears easily ),rough decision method growing up recentlywhich deals with big sample is apt to bring about NP problems in attribute reduction andcharacteristic pick-up etc too, the support vector machine decision method appearing in1995 which deals with small sample is still difficult to choose core function , toconfirm parameters in core function, and to ascertain the way of expanding dimension.So, the existing decision methods can't solve the complex decision problems whichcombine multi- characteristics well. It's urgent to provide a suit of decision methodswhich can dispose decision problems fleetly and synthetically and have diversifiedcharacteristics. Based on great searching for internal and external information and followingclosely international advanced technology, we deeply analyzes the characteristic of thecomplex decision and introduce international advanced thoughts and methods such assoft sets, support vector machine and rough sets into this paper; at the same time manykinds of scientific knowledge for example management, statistics, algebra, artificialintelligence, informatics and so on are melted well. In the systemic point of view, we III重庆大学博士学位论文make emphases to research to distinguish soft information, determine the filter rule formodels or factors and select the decision methods; furthermore we put forward a set ofsoft decision methods based on soft information. Finally using an example forcalculating and simulation and analyzing the results, we prove this set of method has thetheory value and practice value. The main innovation of this paper is as follow:1. Introduce the soft set theory to distinguish the soft information. According to the characteristic of soft information, this paper builds the eigenvector mapping of soft information and then put forward the method of distinguishing soft information based on soft sets theory for the first time, which provides an effective way to distinguish soft information.2. Introduce the soft set theory to apply in the combination forecasting. Bring forward the method of combination forecasting based on rough sets theory, which provides an effective way to filter forecasting model and to make sure the combination coefficient droved by data.3. Introduce the technology of gaining rules in the rough set theory to apply in the multi-factors dynamic forecasting. Making use of the reduction of factors and indexes to build the forecasting model, this paper gets the formula of parameter in forecasting model and reduces calculation of parameter