## 1 Introduction
[16] predicted ship construction man-hours using deployable data at different times during the manufacturing process and converted the categorical variables into dummy variables when processing them. [PERSON] et al. [17] converted binary categorical variables into 1 and 2 labeled coding. [PERSON] et al. [18] set working time and non-working time as 0 and 1 coding, respectively. [PERSON] et al. [19] processed all the categorical variables using one-hot encoding in a binary categorical problem in the presence of categorical variables. All of the above studies adopted the existing conventional encoding methods to carry out the codes for unordered multi-categorical variables but ignored the fact that the encoded values did not maintain the characteristics of unordered multi-categorical variables and also additionally introduced the misleading order and distance bias. In his research on encoding methods for categorical variables, [PERSON] [20] applied five encoding methods to three regression models in his experiments and proved that the regression results varied according to the encoding methods of categorical variables; [PERSON] et al. [21] also verified that the three encoding methods of categorical variables, namely label encoding, one-hot encoding, and embedding, had different effects on the performance of a deep dense neural network and long- and short-term memory neural network models. [PERSON] et al. [22] proposed an ordered log-linear model for ordered categorical variables that can convert ordered categorical data into model-describable multi-categorical lists; [PERSON] et al. [23] used unsupervised embedding to convert categorical variables into word vectors. [PERSON] et al. [24] extended the word-embedding approach by proposing a deep learning method to codify categorical variables. All of the above methods are more suitable for ordered categorical variables or for categorical variables with dependencies. [PERSON] et al. [25] devised a Bayesian network based method for converting categorical variables into data variables, and [PERSON] et al. [26] proposed a method for updating data points in the kernel space of a continuous variable by using SVMs to reflect the effect of each categorical variable; all of the above methods are only suitable for the target value of a binary categorical variable. In general, there is no encoding method for transforming unordered multi-categorical variables into numerical codes with the characteristics of unordered variables and small dimensions. Based on this need, this paper carries out a research on encoding methods for unordered multi-categorical variables. This paper also extends the problem of predicting the working hours of cruise ship production design tasks by analyzing the relationship between task workload and task working hours through the previous working hours data and the design attribute data in the ship area, such as the task type, the number of model structures, the planned working hours, the feedback working hours, etc., so as to provide credible and standards-based working hours solutions for the planning and scheduling of the design tasks when designing and constructing a new ship in the future, which is also the key link to promote the standardization of ship design and construction. In the ship production design task working hour prediction problem, ship area and task type are categorical variables containing dozens of unordered categories, so the unordered multi-categorical variables cannot be ignored. Autoencoder, as a classical tool for feature extraction and data dimensionality reduction, has continued to show its unique value in the field of data processing in recent years. For example, the research of [PERSON] et al. [27] is a profound exploration, in which they cleverly integrate a deep autoencoder network into the Orthogonal Nonnegative Matrix Factorization (ONMF) framework, which achieves an accurate capture and hierarchical parsing of the intrinsic structure of complex data.