The need for a quantitative approach to the morphologic study of ceramics is becoming increasingly evident. Ceramics are the most common material in many archaeological sites and a huge amount of data has accumulated over time. This data can be handled by Machine Learning algorithms which are rapidly gaining popularity in archaeology. Although most approaches can be referred to classification tasks, in this contribution a particular type of Neural Network is proposed for feature extraction from archaeological ceramics. Through this proposed method it is possible to gain a numerical weighted representation of the pottery profile that can be used for multivariate analyses. The case study will focus on regionalisation processes in pottery production between the end of the 2nd millennium BC and the first half of the 1st millennium BC in central Tyrrhenian Italy, a period that saw major transformations in the cultural and socio-political structure of Ancient Italy. The results seem to confirm the regionalisation hypothesis and offer interesting insights into the quantitative study of archaeological ceramics.

对陶瓷形态研究的定量方法的需求变得越来越明显。陶瓷是许多考古遗址中最常见的材料，随着时间的推移积累了大量的数据。这些数据可以通过在考古学中迅速普及的机器学习算法来处理。尽管大多数方法都可以用于分类任务，但在这篇文章中，提出了一种特定类型的神经网络，用于从考古陶瓷中提取特征。通过这种提议的方法，可以获得可用于多变量分析的陶器轮廓的数值加权表示。该案例研究将重点关注公元前 2 世纪末至公元前 1 世纪上半叶在意大利中部第勒尼安地区陶器生产的区域化过程，这一时期见证了古意大利文化和社会政治结构的重大转变. 结果似乎证实了区域化假设，并为考古陶瓷的定量研究提供了有趣的见解。