Research On The Job Fit Evaluation Of The Knowledge Workers

In recent years, human resources play increasingly important role in business operations, and then job fit of employees get more and more important. Knowledge workers have become a kind of critical resource for our society, so evaluation of job fit for them has higher priority. There are many traditional evaluation methods of job fit which are reasonable, but too subjective, such as arrangement method, classification method, scoring method, factor-comparison method, etc. As one branch of artificial intelligence, BP neural network has been applied in management evaluation. So this dissertation applied BP neural network and AHP to research on job fit of the knowledge workersThis dissertation compared the characteristic feature of traditional evaluation methods of job fit, introduced the principle of BP neural network and AHP. According of the chief advantages of the two methods, aimming at the problem of the job fit of the knowledge workers, this dissertation proposed an approach to measure job fit by using AHP and BP neural network compensively. The dissertation showed the process of the method:using of AHP to choose indexes and determine the elements then to evaluate the ob fit of the knowledge workers and at last using the indexes to build BP neural network model which is used to evaluate the job fit of the knowledge workers; combining the results of the two methods to get the final result of evaluation.Then the dissertation through arithmetic simulation to validate the evaluation which the is showwed scientific and effective.Finally the dissertation compared the advantages and disadvantages of three evaluationes grouped based on AHP, BP, AHP-BP which shows that this method not only taking the advantage of AHP evaluation possesses virtues of simple to operation, easy to understand, better to interpret, and comparability but also making use of the advantage of BP neural network esteemed is objective.It enjoies profits both of them skillfully and provides effective rules and methods for enterprises to solve the problem in practice.