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Journal of Chinese
Architecture and Urbanism Tourist perception of calligraphic landscape

Table 1. Frequently used words in web texts about Table 1. (Continued)
calligraphic landscape perception
High-frequency words Time TF-IDF
High-frequency words Time TF-IDF Location 41 0.001255745
Calligraphy 506 0.002025134 Theme 41 0.00132827
Scenic area 332 0.004595631 Famous 39 0.001148361
History 206 0.002921228 Period 39 0.001148361
Check-in 194 0.00288783 Space 38 0.001586702
Culture 190 0.002828287 Square 38 0.001249443
Shaoxing 175 0.004679413 Old street 36 0.001379534
Museum 171 0.003161618 City wall 35 0.001341213
China 164 0.003032195
Xi’an 163 0.004460368 such locations serves to construct personal memories,
Hometown 160 0.004483359 reproduce local collective memory, reinforce self-identity,
Architecture 144 0.002882468 and build collective identity (Shao & Wang, 2023). The
Art 129 0.002536326 ultra-popularity of heritage tourism destinations reflects
Take photos 125 0.002291242 the re-explosion of the long-term accumulation and
inheritance of urban historical memory and traditional
Park 106 0.002597746 culture. The act of “punching cards” digitally embodies a
Place 104 0.001811155 sense of place.
Former residence 99 0.002559754
Atmosphere 98 0.001876285 4.2. Calligraphic landscape perception semantic
Wang xizhi 94 0.002327978 network
Feel 93 0.001895619 High-frequency feature words related to calligraphic
Lu Xun 93 0.003153161 landscapes help analyze the attention hotspots and
distribution patterns in tourists’ online texts. However,
Sage of calligraphy 86 0.002353323 they do not present meaningful associations between
Experience 80 0.002023944 high-frequency feature words and the deeper structural
Orchid pavilion 79 0.002914802 relationships in the texts (Dai & Xue, 2022). To address
Jiangnan 75 0.002028604 this, we constructed a co-occurrence matrix to reveal the
Forest of steles 71 0.002407252 co-occurrence relationship between different words. This
Historic District 65 0.002137205 matrix records how frequently words appear together in
Bookstore 61 0.001976207 the text, allowing us to identify the elements most closely
associated with tourists’ perceptions.
Garden 59 0.001715366
Ancient town 54 0.00195693 Using the text co-occurrence matrix, we applied
Academy 51 0.001605575 the VOSviewer clustering algorithm to analyze highly
relevant words in clusters, constructing the calligraphic
Characteristic 49 0.001295472 landscape perception network. Each cluster represents a
Ancient city 49 0.001587445 specific semantic field or theme that tourists invoke when
Writing 47 0.001593533 describing calligraphic landscapes (Figure 2).
National trend 46 0.001428198 The analysis of the calligraphic landscape perception
Worth 46 0.001258754 network reveals three clusters. The red cluster represents
Bell tower 46 0.001697226 overall perceptions of the calligraphic landscape,
Stone carving 45 0.001502202 focusing on symbolic meanings and summarized as place
Travel 44 0.001247948 recognition and the cultural symbolism of the calligraphic
Master 44 0.001425461 landscape. Conversely, the green and blue clusters exhibit
distinct perceptual characteristics. The green cluster
City 44 0.001247948 reflects experiences of places and landscapes, summarized
Ruins 42 0.001380963 as perceptions of calligraphic landscapes based on heritage
Tradition 42 0.001286373 values. Meanwhile, the blue cluster reflects connections
(Cont'd...) between historical figures and regional culture, summarized

Volume 7 Issue 1 (2025) 5 https://doi.org/10.36922/jcau.3825